Optimization-enabled hybrid deep learning for brain tumor detection and classification from MRI

The precise location of the brain tumor can be found and diagnosed with great accuracy using brain tumor detection and classification. A brain tumor patient's life is prolonged by a few years if it is found and diagnosed early. Medical professionals frequently use magnetic resonance imaging (MR...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Kanchanamala, Pendela [verfasserIn] K.G., Revathi [verfasserIn] Ananth, M. Belsam Jeba [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Magnetic resonance imaging Brain tumor Shepard convolutional neural network Exponential weighed moving average Deer hunting optimization algorithm

Übergeordnetes Werk:	Enthalten in: Biomedical signal processing and control - Amsterdam [u.a.] : Elsevier, 2006, 84
Übergeordnetes Werk:	volume:84

DOI / URN:	10.1016/j.bspc.2023.104955

Katalog-ID:	ELV010047964

Internformat


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520			\|a The precise location of the brain tumor can be found and diagnosed with great accuracy using brain tumor detection and classification. A brain tumor patient's life is prolonged by a few years if it is found and diagnosed early. Medical professionals frequently use magnetic resonance imaging (MRI), one of many imaging modalities, because it can offer contrast information about brain tumors. Exponential deer hunting optimization-based Shepard convolutional neural network (ExpDHO-based ShCNN) is a successful detection method, and ExpDHO-based Deep convolutional neural network (ExpDHO-based Deep CNN) is a successful classification technique, both of which are introduced for the detection and classification of brain tumors, respectively. The noise is removed from the MRI brain images during pre-processing. Following segmentation of the previously processed pictures, augmentation is carried out. ShCNN, which was trained using the created optimization technique known as ExpDHO, is also used for tumor detection. Finally, the created ExpDHO algorithm—a mix of the Exponential weighted moving average (EWMA) and Deer hunting optimization algorithm—is used for tumor categorization (DHOA). Additionally, the created technique delivered beneficial results based on performance metrics, such as accuracy, sensitivity, and specificity, with higher values of 0.929, 0.934, and 0.939 for brain tumor detection and 0.917, 0.918, and 0.919 for brain tumor classification, respectively.
650		4	\|a Magnetic resonance imaging
650		4	\|a Brain tumor
650		4	\|a Shepard convolutional neural network
650		4	\|a Exponential weighed moving average
650		4	\|a Deer hunting optimization algorithm
700	1		\|a K.G., Revathi \|e verfasserin \|4 aut
700	1		\|a Ananth, M. Belsam Jeba \|e verfasserin \|4 aut
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publishDate	2023
allfields	10.1016/j.bspc.2023.104955 doi (DE-627)ELV010047964 (ELSEVIER)S1746-8094(23)00388-9 DE-627 ger DE-627 rda eng 610 VZ 44.09 bkl 44.32 bkl Kanchanamala, Pendela verfasserin aut Optimization-enabled hybrid deep learning for brain tumor detection and classification from MRI 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The precise location of the brain tumor can be found and diagnosed with great accuracy using brain tumor detection and classification. A brain tumor patient's life is prolonged by a few years if it is found and diagnosed early. Medical professionals frequently use magnetic resonance imaging (MRI), one of many imaging modalities, because it can offer contrast information about brain tumors. Exponential deer hunting optimization-based Shepard convolutional neural network (ExpDHO-based ShCNN) is a successful detection method, and ExpDHO-based Deep convolutional neural network (ExpDHO-based Deep CNN) is a successful classification technique, both of which are introduced for the detection and classification of brain tumors, respectively. The noise is removed from the MRI brain images during pre-processing. Following segmentation of the previously processed pictures, augmentation is carried out. ShCNN, which was trained using the created optimization technique known as ExpDHO, is also used for tumor detection. Finally, the created ExpDHO algorithm—a mix of the Exponential weighted moving average (EWMA) and Deer hunting optimization algorithm—is used for tumor categorization (DHOA). Additionally, the created technique delivered beneficial results based on performance metrics, such as accuracy, sensitivity, and specificity, with higher values of 0.929, 0.934, and 0.939 for brain tumor detection and 0.917, 0.918, and 0.919 for brain tumor classification, respectively. Magnetic resonance imaging Brain tumor Shepard convolutional neural network Exponential weighed moving average Deer hunting optimization algorithm K.G., Revathi verfasserin aut Ananth, M. Belsam Jeba verfasserin aut Enthalten in Biomedical signal processing and control Amsterdam [u.a.] : Elsevier, 2006 84 Online-Ressource (DE-627)515537861 (DE-600)2241886-6 (DE-576)261592653 1746-8108 nnns volume:84 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.09 Medizintechnik VZ 44.32 Medizinische Mathematik medizinische Statistik VZ AR 84
spelling	10.1016/j.bspc.2023.104955 doi (DE-627)ELV010047964 (ELSEVIER)S1746-8094(23)00388-9 DE-627 ger DE-627 rda eng 610 VZ 44.09 bkl 44.32 bkl Kanchanamala, Pendela verfasserin aut Optimization-enabled hybrid deep learning for brain tumor detection and classification from MRI 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The precise location of the brain tumor can be found and diagnosed with great accuracy using brain tumor detection and classification. A brain tumor patient's life is prolonged by a few years if it is found and diagnosed early. Medical professionals frequently use magnetic resonance imaging (MRI), one of many imaging modalities, because it can offer contrast information about brain tumors. Exponential deer hunting optimization-based Shepard convolutional neural network (ExpDHO-based ShCNN) is a successful detection method, and ExpDHO-based Deep convolutional neural network (ExpDHO-based Deep CNN) is a successful classification technique, both of which are introduced for the detection and classification of brain tumors, respectively. The noise is removed from the MRI brain images during pre-processing. Following segmentation of the previously processed pictures, augmentation is carried out. ShCNN, which was trained using the created optimization technique known as ExpDHO, is also used for tumor detection. Finally, the created ExpDHO algorithm—a mix of the Exponential weighted moving average (EWMA) and Deer hunting optimization algorithm—is used for tumor categorization (DHOA). Additionally, the created technique delivered beneficial results based on performance metrics, such as accuracy, sensitivity, and specificity, with higher values of 0.929, 0.934, and 0.939 for brain tumor detection and 0.917, 0.918, and 0.919 for brain tumor classification, respectively. Magnetic resonance imaging Brain tumor Shepard convolutional neural network Exponential weighed moving average Deer hunting optimization algorithm K.G., Revathi verfasserin aut Ananth, M. Belsam Jeba verfasserin aut Enthalten in Biomedical signal processing and control Amsterdam [u.a.] : Elsevier, 2006 84 Online-Ressource (DE-627)515537861 (DE-600)2241886-6 (DE-576)261592653 1746-8108 nnns volume:84 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.09 Medizintechnik VZ 44.32 Medizinische Mathematik medizinische Statistik VZ AR 84
allfields_unstemmed	10.1016/j.bspc.2023.104955 doi (DE-627)ELV010047964 (ELSEVIER)S1746-8094(23)00388-9 DE-627 ger DE-627 rda eng 610 VZ 44.09 bkl 44.32 bkl Kanchanamala, Pendela verfasserin aut Optimization-enabled hybrid deep learning for brain tumor detection and classification from MRI 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The precise location of the brain tumor can be found and diagnosed with great accuracy using brain tumor detection and classification. A brain tumor patient's life is prolonged by a few years if it is found and diagnosed early. Medical professionals frequently use magnetic resonance imaging (MRI), one of many imaging modalities, because it can offer contrast information about brain tumors. Exponential deer hunting optimization-based Shepard convolutional neural network (ExpDHO-based ShCNN) is a successful detection method, and ExpDHO-based Deep convolutional neural network (ExpDHO-based Deep CNN) is a successful classification technique, both of which are introduced for the detection and classification of brain tumors, respectively. The noise is removed from the MRI brain images during pre-processing. Following segmentation of the previously processed pictures, augmentation is carried out. ShCNN, which was trained using the created optimization technique known as ExpDHO, is also used for tumor detection. Finally, the created ExpDHO algorithm—a mix of the Exponential weighted moving average (EWMA) and Deer hunting optimization algorithm—is used for tumor categorization (DHOA). Additionally, the created technique delivered beneficial results based on performance metrics, such as accuracy, sensitivity, and specificity, with higher values of 0.929, 0.934, and 0.939 for brain tumor detection and 0.917, 0.918, and 0.919 for brain tumor classification, respectively. Magnetic resonance imaging Brain tumor Shepard convolutional neural network Exponential weighed moving average Deer hunting optimization algorithm K.G., Revathi verfasserin aut Ananth, M. Belsam Jeba verfasserin aut Enthalten in Biomedical signal processing and control Amsterdam [u.a.] : Elsevier, 2006 84 Online-Ressource (DE-627)515537861 (DE-600)2241886-6 (DE-576)261592653 1746-8108 nnns volume:84 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.09 Medizintechnik VZ 44.32 Medizinische Mathematik medizinische Statistik VZ AR 84
allfieldsGer	10.1016/j.bspc.2023.104955 doi (DE-627)ELV010047964 (ELSEVIER)S1746-8094(23)00388-9 DE-627 ger DE-627 rda eng 610 VZ 44.09 bkl 44.32 bkl Kanchanamala, Pendela verfasserin aut Optimization-enabled hybrid deep learning for brain tumor detection and classification from MRI 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The precise location of the brain tumor can be found and diagnosed with great accuracy using brain tumor detection and classification. A brain tumor patient's life is prolonged by a few years if it is found and diagnosed early. Medical professionals frequently use magnetic resonance imaging (MRI), one of many imaging modalities, because it can offer contrast information about brain tumors. Exponential deer hunting optimization-based Shepard convolutional neural network (ExpDHO-based ShCNN) is a successful detection method, and ExpDHO-based Deep convolutional neural network (ExpDHO-based Deep CNN) is a successful classification technique, both of which are introduced for the detection and classification of brain tumors, respectively. The noise is removed from the MRI brain images during pre-processing. Following segmentation of the previously processed pictures, augmentation is carried out. ShCNN, which was trained using the created optimization technique known as ExpDHO, is also used for tumor detection. Finally, the created ExpDHO algorithm—a mix of the Exponential weighted moving average (EWMA) and Deer hunting optimization algorithm—is used for tumor categorization (DHOA). Additionally, the created technique delivered beneficial results based on performance metrics, such as accuracy, sensitivity, and specificity, with higher values of 0.929, 0.934, and 0.939 for brain tumor detection and 0.917, 0.918, and 0.919 for brain tumor classification, respectively. Magnetic resonance imaging Brain tumor Shepard convolutional neural network Exponential weighed moving average Deer hunting optimization algorithm K.G., Revathi verfasserin aut Ananth, M. Belsam Jeba verfasserin aut Enthalten in Biomedical signal processing and control Amsterdam [u.a.] : Elsevier, 2006 84 Online-Ressource (DE-627)515537861 (DE-600)2241886-6 (DE-576)261592653 1746-8108 nnns volume:84 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.09 Medizintechnik VZ 44.32 Medizinische Mathematik medizinische Statistik VZ AR 84
allfieldsSound	10.1016/j.bspc.2023.104955 doi (DE-627)ELV010047964 (ELSEVIER)S1746-8094(23)00388-9 DE-627 ger DE-627 rda eng 610 VZ 44.09 bkl 44.32 bkl Kanchanamala, Pendela verfasserin aut Optimization-enabled hybrid deep learning for brain tumor detection and classification from MRI 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier The precise location of the brain tumor can be found and diagnosed with great accuracy using brain tumor detection and classification. A brain tumor patient's life is prolonged by a few years if it is found and diagnosed early. Medical professionals frequently use magnetic resonance imaging (MRI), one of many imaging modalities, because it can offer contrast information about brain tumors. Exponential deer hunting optimization-based Shepard convolutional neural network (ExpDHO-based ShCNN) is a successful detection method, and ExpDHO-based Deep convolutional neural network (ExpDHO-based Deep CNN) is a successful classification technique, both of which are introduced for the detection and classification of brain tumors, respectively. The noise is removed from the MRI brain images during pre-processing. Following segmentation of the previously processed pictures, augmentation is carried out. ShCNN, which was trained using the created optimization technique known as ExpDHO, is also used for tumor detection. Finally, the created ExpDHO algorithm—a mix of the Exponential weighted moving average (EWMA) and Deer hunting optimization algorithm—is used for tumor categorization (DHOA). Additionally, the created technique delivered beneficial results based on performance metrics, such as accuracy, sensitivity, and specificity, with higher values of 0.929, 0.934, and 0.939 for brain tumor detection and 0.917, 0.918, and 0.919 for brain tumor classification, respectively. Magnetic resonance imaging Brain tumor Shepard convolutional neural network Exponential weighed moving average Deer hunting optimization algorithm K.G., Revathi verfasserin aut Ananth, M. Belsam Jeba verfasserin aut Enthalten in Biomedical signal processing and control Amsterdam [u.a.] : Elsevier, 2006 84 Online-Ressource (DE-627)515537861 (DE-600)2241886-6 (DE-576)261592653 1746-8108 nnns volume:84 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 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_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.09 Medizintechnik VZ 44.32 Medizinische Mathematik medizinische Statistik VZ AR 84
language	English
source	Enthalten in Biomedical signal processing and control 84 volume:84
sourceStr	Enthalten in Biomedical signal processing and control 84 volume:84
format_phy_str_mv	Article
bklname	Medizintechnik Medizinische Mathematik medizinische Statistik
institution	findex.gbv.de
topic_facet	Magnetic resonance imaging Brain tumor Shepard convolutional neural network Exponential weighed moving average Deer hunting optimization algorithm
dewey-raw	610
isfreeaccess_bool	false
container_title	Biomedical signal processing and control
authorswithroles_txt_mv	Kanchanamala, Pendela @@aut@@ K.G., Revathi @@aut@@ Ananth, M. Belsam Jeba @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	515537861
dewey-sort	3610
id	ELV010047964
language_de	englisch
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author	Kanchanamala, Pendela
spellingShingle	Kanchanamala, Pendela ddc 610 bkl 44.09 bkl 44.32 misc Magnetic resonance imaging misc Brain tumor misc Shepard convolutional neural network misc Exponential weighed moving average misc Deer hunting optimization algorithm Optimization-enabled hybrid deep learning for brain tumor detection and classification from MRI
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topic_title	610 VZ 44.09 bkl 44.32 bkl Optimization-enabled hybrid deep learning for brain tumor detection and classification from MRI Magnetic resonance imaging Brain tumor Shepard convolutional neural network Exponential weighed moving average Deer hunting optimization algorithm
topic	ddc 610 bkl 44.09 bkl 44.32 misc Magnetic resonance imaging misc Brain tumor misc Shepard convolutional neural network misc Exponential weighed moving average misc Deer hunting optimization algorithm
topic_unstemmed	ddc 610 bkl 44.09 bkl 44.32 misc Magnetic resonance imaging misc Brain tumor misc Shepard convolutional neural network misc Exponential weighed moving average misc Deer hunting optimization algorithm
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title	Optimization-enabled hybrid deep learning for brain tumor detection and classification from MRI
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title_full	Optimization-enabled hybrid deep learning for brain tumor detection and classification from MRI
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title_sort	optimization-enabled hybrid deep learning for brain tumor detection and classification from mri
title_auth	Optimization-enabled hybrid deep learning for brain tumor detection and classification from MRI
abstract	The precise location of the brain tumor can be found and diagnosed with great accuracy using brain tumor detection and classification. A brain tumor patient's life is prolonged by a few years if it is found and diagnosed early. Medical professionals frequently use magnetic resonance imaging (MRI), one of many imaging modalities, because it can offer contrast information about brain tumors. Exponential deer hunting optimization-based Shepard convolutional neural network (ExpDHO-based ShCNN) is a successful detection method, and ExpDHO-based Deep convolutional neural network (ExpDHO-based Deep CNN) is a successful classification technique, both of which are introduced for the detection and classification of brain tumors, respectively. The noise is removed from the MRI brain images during pre-processing. Following segmentation of the previously processed pictures, augmentation is carried out. ShCNN, which was trained using the created optimization technique known as ExpDHO, is also used for tumor detection. Finally, the created ExpDHO algorithm—a mix of the Exponential weighted moving average (EWMA) and Deer hunting optimization algorithm—is used for tumor categorization (DHOA). Additionally, the created technique delivered beneficial results based on performance metrics, such as accuracy, sensitivity, and specificity, with higher values of 0.929, 0.934, and 0.939 for brain tumor detection and 0.917, 0.918, and 0.919 for brain tumor classification, respectively.
abstractGer	The precise location of the brain tumor can be found and diagnosed with great accuracy using brain tumor detection and classification. A brain tumor patient's life is prolonged by a few years if it is found and diagnosed early. Medical professionals frequently use magnetic resonance imaging (MRI), one of many imaging modalities, because it can offer contrast information about brain tumors. Exponential deer hunting optimization-based Shepard convolutional neural network (ExpDHO-based ShCNN) is a successful detection method, and ExpDHO-based Deep convolutional neural network (ExpDHO-based Deep CNN) is a successful classification technique, both of which are introduced for the detection and classification of brain tumors, respectively. The noise is removed from the MRI brain images during pre-processing. Following segmentation of the previously processed pictures, augmentation is carried out. ShCNN, which was trained using the created optimization technique known as ExpDHO, is also used for tumor detection. Finally, the created ExpDHO algorithm—a mix of the Exponential weighted moving average (EWMA) and Deer hunting optimization algorithm—is used for tumor categorization (DHOA). Additionally, the created technique delivered beneficial results based on performance metrics, such as accuracy, sensitivity, and specificity, with higher values of 0.929, 0.934, and 0.939 for brain tumor detection and 0.917, 0.918, and 0.919 for brain tumor classification, respectively.
abstract_unstemmed	The precise location of the brain tumor can be found and diagnosed with great accuracy using brain tumor detection and classification. A brain tumor patient's life is prolonged by a few years if it is found and diagnosed early. Medical professionals frequently use magnetic resonance imaging (MRI), one of many imaging modalities, because it can offer contrast information about brain tumors. Exponential deer hunting optimization-based Shepard convolutional neural network (ExpDHO-based ShCNN) is a successful detection method, and ExpDHO-based Deep convolutional neural network (ExpDHO-based Deep CNN) is a successful classification technique, both of which are introduced for the detection and classification of brain tumors, respectively. The noise is removed from the MRI brain images during pre-processing. Following segmentation of the previously processed pictures, augmentation is carried out. ShCNN, which was trained using the created optimization technique known as ExpDHO, is also used for tumor detection. Finally, the created ExpDHO algorithm—a mix of the Exponential weighted moving average (EWMA) and Deer hunting optimization algorithm—is used for tumor categorization (DHOA). Additionally, the created technique delivered beneficial results based on performance metrics, such as accuracy, sensitivity, and specificity, with higher values of 0.929, 0.934, and 0.939 for brain tumor detection and 0.917, 0.918, and 0.919 for brain tumor classification, respectively.
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title_short	Optimization-enabled hybrid deep learning for brain tumor detection and classification from MRI
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author2	K.G., Revathi Ananth, M. Belsam Jeba
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Optimization-enabled hybrid deep learning for brain tumor detection and classification from MRI

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