Automated detection and classification of psoriasis types using deep neural networks from dermatology images

Abstract Psoriasis is a chronic inflammatory disease that significantly affects the patient's living standard. Dermatologists examine the skin lesions visually to determine the condition. Guttate psoriasis, flexural or inverse psoriasis, pustular psoriasis, erythrodermic psoriasis, psoriatic ar...
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Autor*in:	Rashid, Muhammad Sajid [verfasserIn] Gilanie, Ghulam Naveed, Saira Cheema, Sana Sajid, Muhammad

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Dermoscopic images Deep neural network Psoriasis type’s classification

Anmerkung:	© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: Signal, image and video processing - London [u.a.] : Springer, 2007, 18(2023), 1 vom: 23. Aug., Seite 163-172
Übergeordnetes Werk:	volume:18 ; year:2023 ; number:1 ; day:23 ; month:08 ; pages:163-172

Links:	Volltext

DOI / URN:	10.1007/s11760-023-02722-9

Katalog-ID:	SPR054522811

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520			\|a Abstract Psoriasis is a chronic inflammatory disease that significantly affects the patient's living standard. Dermatologists examine the skin lesions visually to determine the condition. Guttate psoriasis, flexural or inverse psoriasis, pustular psoriasis, erythrodermic psoriasis, psoriatic arthritis, etc., are the most common skin conditions affecting people worldwide. Dermoscopic images are used for clinical diagnosis of these conditions, which require a specialized setup with various medical equipment for better understanding. Patients with skin conditions in Pakistan don't bother going to clinics; they might have serious skin conditions. In this study, a light-weighted deep neural network (DNN)-based model has been developed with fewer but learnable parameters. The results obtained with the proposed DNN model have been compared with the state-of-the-art pre-trained models, i.e., Googlenet, InceptionV3, and VGG-19. Benchmarked, publicly available datasets have been used for experiments. The datasets included in the investigation contain RGB images and are converted into YCbCr for better classification. Standard evaluation parameters, i.e., accuracy, specificity, sensitivity, and area under the curve (AUC), have been used to evaluate the proposed DNN-based model. The proposed model for all psoriasis types achieves the best classification performance. The highest one is the case of psoriatic arthritis, where these measures are (accuracy = 99.89%, specificity = 99.08%, sensitivity = 99.0%, and AUC = 0.99). Using the proposed model, it is demonstrated that YCbcr is the best color space for identifying psoriasis and its types.
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allfields	10.1007/s11760-023-02722-9 doi (DE-627)SPR054522811 (SPR)s11760-023-02722-9-e DE-627 ger DE-627 rakwb eng Rashid, Muhammad Sajid verfasserin aut Automated detection and classification of psoriasis types using deep neural networks from dermatology images 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Psoriasis is a chronic inflammatory disease that significantly affects the patient's living standard. Dermatologists examine the skin lesions visually to determine the condition. Guttate psoriasis, flexural or inverse psoriasis, pustular psoriasis, erythrodermic psoriasis, psoriatic arthritis, etc., are the most common skin conditions affecting people worldwide. Dermoscopic images are used for clinical diagnosis of these conditions, which require a specialized setup with various medical equipment for better understanding. Patients with skin conditions in Pakistan don't bother going to clinics; they might have serious skin conditions. In this study, a light-weighted deep neural network (DNN)-based model has been developed with fewer but learnable parameters. The results obtained with the proposed DNN model have been compared with the state-of-the-art pre-trained models, i.e., Googlenet, InceptionV3, and VGG-19. Benchmarked, publicly available datasets have been used for experiments. The datasets included in the investigation contain RGB images and are converted into YCbCr for better classification. Standard evaluation parameters, i.e., accuracy, specificity, sensitivity, and area under the curve (AUC), have been used to evaluate the proposed DNN-based model. The proposed model for all psoriasis types achieves the best classification performance. The highest one is the case of psoriatic arthritis, where these measures are (accuracy = 99.89%, specificity = 99.08%, sensitivity = 99.0%, and AUC = 0.99). Using the proposed model, it is demonstrated that YCbcr is the best color space for identifying psoriasis and its types. Dermoscopic images (dpeaa)DE-He213 Deep neural network (dpeaa)DE-He213 Psoriasis type’s classification (dpeaa)DE-He213 Gilanie, Ghulam aut Naveed, Saira aut Cheema, Sana aut Sajid, Muhammad aut Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 18(2023), 1 vom: 23. Aug., Seite 163-172 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:18 year:2023 number:1 day:23 month:08 pages:163-172 https://dx.doi.org/10.1007/s11760-023-02722-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 18 2023 1 23 08 163-172
spelling	10.1007/s11760-023-02722-9 doi (DE-627)SPR054522811 (SPR)s11760-023-02722-9-e DE-627 ger DE-627 rakwb eng Rashid, Muhammad Sajid verfasserin aut Automated detection and classification of psoriasis types using deep neural networks from dermatology images 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Psoriasis is a chronic inflammatory disease that significantly affects the patient's living standard. Dermatologists examine the skin lesions visually to determine the condition. Guttate psoriasis, flexural or inverse psoriasis, pustular psoriasis, erythrodermic psoriasis, psoriatic arthritis, etc., are the most common skin conditions affecting people worldwide. Dermoscopic images are used for clinical diagnosis of these conditions, which require a specialized setup with various medical equipment for better understanding. Patients with skin conditions in Pakistan don't bother going to clinics; they might have serious skin conditions. In this study, a light-weighted deep neural network (DNN)-based model has been developed with fewer but learnable parameters. The results obtained with the proposed DNN model have been compared with the state-of-the-art pre-trained models, i.e., Googlenet, InceptionV3, and VGG-19. Benchmarked, publicly available datasets have been used for experiments. The datasets included in the investigation contain RGB images and are converted into YCbCr for better classification. Standard evaluation parameters, i.e., accuracy, specificity, sensitivity, and area under the curve (AUC), have been used to evaluate the proposed DNN-based model. The proposed model for all psoriasis types achieves the best classification performance. The highest one is the case of psoriatic arthritis, where these measures are (accuracy = 99.89%, specificity = 99.08%, sensitivity = 99.0%, and AUC = 0.99). Using the proposed model, it is demonstrated that YCbcr is the best color space for identifying psoriasis and its types. Dermoscopic images (dpeaa)DE-He213 Deep neural network (dpeaa)DE-He213 Psoriasis type’s classification (dpeaa)DE-He213 Gilanie, Ghulam aut Naveed, Saira aut Cheema, Sana aut Sajid, Muhammad aut Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 18(2023), 1 vom: 23. Aug., Seite 163-172 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:18 year:2023 number:1 day:23 month:08 pages:163-172 https://dx.doi.org/10.1007/s11760-023-02722-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 18 2023 1 23 08 163-172
allfields_unstemmed	10.1007/s11760-023-02722-9 doi (DE-627)SPR054522811 (SPR)s11760-023-02722-9-e DE-627 ger DE-627 rakwb eng Rashid, Muhammad Sajid verfasserin aut Automated detection and classification of psoriasis types using deep neural networks from dermatology images 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Psoriasis is a chronic inflammatory disease that significantly affects the patient's living standard. Dermatologists examine the skin lesions visually to determine the condition. Guttate psoriasis, flexural or inverse psoriasis, pustular psoriasis, erythrodermic psoriasis, psoriatic arthritis, etc., are the most common skin conditions affecting people worldwide. Dermoscopic images are used for clinical diagnosis of these conditions, which require a specialized setup with various medical equipment for better understanding. Patients with skin conditions in Pakistan don't bother going to clinics; they might have serious skin conditions. In this study, a light-weighted deep neural network (DNN)-based model has been developed with fewer but learnable parameters. The results obtained with the proposed DNN model have been compared with the state-of-the-art pre-trained models, i.e., Googlenet, InceptionV3, and VGG-19. Benchmarked, publicly available datasets have been used for experiments. The datasets included in the investigation contain RGB images and are converted into YCbCr for better classification. Standard evaluation parameters, i.e., accuracy, specificity, sensitivity, and area under the curve (AUC), have been used to evaluate the proposed DNN-based model. The proposed model for all psoriasis types achieves the best classification performance. The highest one is the case of psoriatic arthritis, where these measures are (accuracy = 99.89%, specificity = 99.08%, sensitivity = 99.0%, and AUC = 0.99). Using the proposed model, it is demonstrated that YCbcr is the best color space for identifying psoriasis and its types. Dermoscopic images (dpeaa)DE-He213 Deep neural network (dpeaa)DE-He213 Psoriasis type’s classification (dpeaa)DE-He213 Gilanie, Ghulam aut Naveed, Saira aut Cheema, Sana aut Sajid, Muhammad aut Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 18(2023), 1 vom: 23. Aug., Seite 163-172 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:18 year:2023 number:1 day:23 month:08 pages:163-172 https://dx.doi.org/10.1007/s11760-023-02722-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 18 2023 1 23 08 163-172
allfieldsGer	10.1007/s11760-023-02722-9 doi (DE-627)SPR054522811 (SPR)s11760-023-02722-9-e DE-627 ger DE-627 rakwb eng Rashid, Muhammad Sajid verfasserin aut Automated detection and classification of psoriasis types using deep neural networks from dermatology images 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Psoriasis is a chronic inflammatory disease that significantly affects the patient's living standard. Dermatologists examine the skin lesions visually to determine the condition. Guttate psoriasis, flexural or inverse psoriasis, pustular psoriasis, erythrodermic psoriasis, psoriatic arthritis, etc., are the most common skin conditions affecting people worldwide. Dermoscopic images are used for clinical diagnosis of these conditions, which require a specialized setup with various medical equipment for better understanding. Patients with skin conditions in Pakistan don't bother going to clinics; they might have serious skin conditions. In this study, a light-weighted deep neural network (DNN)-based model has been developed with fewer but learnable parameters. The results obtained with the proposed DNN model have been compared with the state-of-the-art pre-trained models, i.e., Googlenet, InceptionV3, and VGG-19. Benchmarked, publicly available datasets have been used for experiments. The datasets included in the investigation contain RGB images and are converted into YCbCr for better classification. Standard evaluation parameters, i.e., accuracy, specificity, sensitivity, and area under the curve (AUC), have been used to evaluate the proposed DNN-based model. The proposed model for all psoriasis types achieves the best classification performance. The highest one is the case of psoriatic arthritis, where these measures are (accuracy = 99.89%, specificity = 99.08%, sensitivity = 99.0%, and AUC = 0.99). Using the proposed model, it is demonstrated that YCbcr is the best color space for identifying psoriasis and its types. Dermoscopic images (dpeaa)DE-He213 Deep neural network (dpeaa)DE-He213 Psoriasis type’s classification (dpeaa)DE-He213 Gilanie, Ghulam aut Naveed, Saira aut Cheema, Sana aut Sajid, Muhammad aut Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 18(2023), 1 vom: 23. Aug., Seite 163-172 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:18 year:2023 number:1 day:23 month:08 pages:163-172 https://dx.doi.org/10.1007/s11760-023-02722-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 18 2023 1 23 08 163-172
allfieldsSound	10.1007/s11760-023-02722-9 doi (DE-627)SPR054522811 (SPR)s11760-023-02722-9-e DE-627 ger DE-627 rakwb eng Rashid, Muhammad Sajid verfasserin aut Automated detection and classification of psoriasis types using deep neural networks from dermatology images 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Psoriasis is a chronic inflammatory disease that significantly affects the patient's living standard. Dermatologists examine the skin lesions visually to determine the condition. Guttate psoriasis, flexural or inverse psoriasis, pustular psoriasis, erythrodermic psoriasis, psoriatic arthritis, etc., are the most common skin conditions affecting people worldwide. Dermoscopic images are used for clinical diagnosis of these conditions, which require a specialized setup with various medical equipment for better understanding. Patients with skin conditions in Pakistan don't bother going to clinics; they might have serious skin conditions. In this study, a light-weighted deep neural network (DNN)-based model has been developed with fewer but learnable parameters. The results obtained with the proposed DNN model have been compared with the state-of-the-art pre-trained models, i.e., Googlenet, InceptionV3, and VGG-19. Benchmarked, publicly available datasets have been used for experiments. The datasets included in the investigation contain RGB images and are converted into YCbCr for better classification. Standard evaluation parameters, i.e., accuracy, specificity, sensitivity, and area under the curve (AUC), have been used to evaluate the proposed DNN-based model. The proposed model for all psoriasis types achieves the best classification performance. The highest one is the case of psoriatic arthritis, where these measures are (accuracy = 99.89%, specificity = 99.08%, sensitivity = 99.0%, and AUC = 0.99). Using the proposed model, it is demonstrated that YCbcr is the best color space for identifying psoriasis and its types. Dermoscopic images (dpeaa)DE-He213 Deep neural network (dpeaa)DE-He213 Psoriasis type’s classification (dpeaa)DE-He213 Gilanie, Ghulam aut Naveed, Saira aut Cheema, Sana aut Sajid, Muhammad aut Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 18(2023), 1 vom: 23. Aug., Seite 163-172 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:18 year:2023 number:1 day:23 month:08 pages:163-172 https://dx.doi.org/10.1007/s11760-023-02722-9 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 18 2023 1 23 08 163-172
language	English
source	Enthalten in Signal, image and video processing 18(2023), 1 vom: 23. Aug., Seite 163-172 volume:18 year:2023 number:1 day:23 month:08 pages:163-172
sourceStr	Enthalten in Signal, image and video processing 18(2023), 1 vom: 23. Aug., Seite 163-172 volume:18 year:2023 number:1 day:23 month:08 pages:163-172
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Dermoscopic images Deep neural network Psoriasis type’s classification
isfreeaccess_bool	false
container_title	Signal, image and video processing
authorswithroles_txt_mv	Rashid, Muhammad Sajid @@aut@@ Gilanie, Ghulam @@aut@@ Naveed, Saira @@aut@@ Cheema, Sana @@aut@@ Sajid, Muhammad @@aut@@
publishDateDaySort_date	2023-08-23T00:00:00Z
hierarchy_top_id	546899102
id	SPR054522811
language_de	englisch
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author	Rashid, Muhammad Sajid
spellingShingle	Rashid, Muhammad Sajid misc Dermoscopic images misc Deep neural network misc Psoriasis type’s classification Automated detection and classification of psoriasis types using deep neural networks from dermatology images
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topic_title	Automated detection and classification of psoriasis types using deep neural networks from dermatology images Dermoscopic images (dpeaa)DE-He213 Deep neural network (dpeaa)DE-He213 Psoriasis type’s classification (dpeaa)DE-He213
topic	misc Dermoscopic images misc Deep neural network misc Psoriasis type’s classification
topic_unstemmed	misc Dermoscopic images misc Deep neural network misc Psoriasis type’s classification
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title	Automated detection and classification of psoriasis types using deep neural networks from dermatology images
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title_full	Automated detection and classification of psoriasis types using deep neural networks from dermatology images
author_sort	Rashid, Muhammad Sajid
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author_browse	Rashid, Muhammad Sajid Gilanie, Ghulam Naveed, Saira Cheema, Sana Sajid, Muhammad
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author-letter	Rashid, Muhammad Sajid
doi_str_mv	10.1007/s11760-023-02722-9
title_sort	automated detection and classification of psoriasis types using deep neural networks from dermatology images
title_auth	Automated detection and classification of psoriasis types using deep neural networks from dermatology images
abstract	Abstract Psoriasis is a chronic inflammatory disease that significantly affects the patient's living standard. Dermatologists examine the skin lesions visually to determine the condition. Guttate psoriasis, flexural or inverse psoriasis, pustular psoriasis, erythrodermic psoriasis, psoriatic arthritis, etc., are the most common skin conditions affecting people worldwide. Dermoscopic images are used for clinical diagnosis of these conditions, which require a specialized setup with various medical equipment for better understanding. Patients with skin conditions in Pakistan don't bother going to clinics; they might have serious skin conditions. In this study, a light-weighted deep neural network (DNN)-based model has been developed with fewer but learnable parameters. The results obtained with the proposed DNN model have been compared with the state-of-the-art pre-trained models, i.e., Googlenet, InceptionV3, and VGG-19. Benchmarked, publicly available datasets have been used for experiments. The datasets included in the investigation contain RGB images and are converted into YCbCr for better classification. Standard evaluation parameters, i.e., accuracy, specificity, sensitivity, and area under the curve (AUC), have been used to evaluate the proposed DNN-based model. The proposed model for all psoriasis types achieves the best classification performance. The highest one is the case of psoriatic arthritis, where these measures are (accuracy = 99.89%, specificity = 99.08%, sensitivity = 99.0%, and AUC = 0.99). Using the proposed model, it is demonstrated that YCbcr is the best color space for identifying psoriasis and its types. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstractGer	Abstract Psoriasis is a chronic inflammatory disease that significantly affects the patient's living standard. Dermatologists examine the skin lesions visually to determine the condition. Guttate psoriasis, flexural or inverse psoriasis, pustular psoriasis, erythrodermic psoriasis, psoriatic arthritis, etc., are the most common skin conditions affecting people worldwide. Dermoscopic images are used for clinical diagnosis of these conditions, which require a specialized setup with various medical equipment for better understanding. Patients with skin conditions in Pakistan don't bother going to clinics; they might have serious skin conditions. In this study, a light-weighted deep neural network (DNN)-based model has been developed with fewer but learnable parameters. The results obtained with the proposed DNN model have been compared with the state-of-the-art pre-trained models, i.e., Googlenet, InceptionV3, and VGG-19. Benchmarked, publicly available datasets have been used for experiments. The datasets included in the investigation contain RGB images and are converted into YCbCr for better classification. Standard evaluation parameters, i.e., accuracy, specificity, sensitivity, and area under the curve (AUC), have been used to evaluate the proposed DNN-based model. The proposed model for all psoriasis types achieves the best classification performance. The highest one is the case of psoriatic arthritis, where these measures are (accuracy = 99.89%, specificity = 99.08%, sensitivity = 99.0%, and AUC = 0.99). Using the proposed model, it is demonstrated that YCbcr is the best color space for identifying psoriasis and its types. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstract_unstemmed	Abstract Psoriasis is a chronic inflammatory disease that significantly affects the patient's living standard. Dermatologists examine the skin lesions visually to determine the condition. Guttate psoriasis, flexural or inverse psoriasis, pustular psoriasis, erythrodermic psoriasis, psoriatic arthritis, etc., are the most common skin conditions affecting people worldwide. Dermoscopic images are used for clinical diagnosis of these conditions, which require a specialized setup with various medical equipment for better understanding. Patients with skin conditions in Pakistan don't bother going to clinics; they might have serious skin conditions. In this study, a light-weighted deep neural network (DNN)-based model has been developed with fewer but learnable parameters. The results obtained with the proposed DNN model have been compared with the state-of-the-art pre-trained models, i.e., Googlenet, InceptionV3, and VGG-19. Benchmarked, publicly available datasets have been used for experiments. The datasets included in the investigation contain RGB images and are converted into YCbCr for better classification. Standard evaluation parameters, i.e., accuracy, specificity, sensitivity, and area under the curve (AUC), have been used to evaluate the proposed DNN-based model. The proposed model for all psoriasis types achieves the best classification performance. The highest one is the case of psoriatic arthritis, where these measures are (accuracy = 99.89%, specificity = 99.08%, sensitivity = 99.0%, and AUC = 0.99). Using the proposed model, it is demonstrated that YCbcr is the best color space for identifying psoriasis and its types. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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title_short	Automated detection and classification of psoriasis types using deep neural networks from dermatology images
url	https://dx.doi.org/10.1007/s11760-023-02722-9
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author2	Gilanie, Ghulam Naveed, Saira Cheema, Sana Sajid, Muhammad
author2Str	Gilanie, Ghulam Naveed, Saira Cheema, Sana Sajid, Muhammad
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doi_str	10.1007/s11760-023-02722-9
up_date	2024-07-04T02:01:17.520Z
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Automated detection and classification of psoriasis types using deep neural networks from dermatology images

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