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Title |
Medical Image Classification using Pre-trained ??Convolutional Neural ?Networks and Support ?Vector ?Machine |
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Author |
Ali Ahmed |
| Citation |
Vol. 21 No. 6 pp. 1-6 |
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Abstract |
Recently, pre-trained convolutional neural network ?CNNs have been widely used ?and applied ?for medical image ?classification. These models can utilised in ?three different ways, ?for feature extraction, ?to use the architecture of ?the pre-trained ?model and to train some layers while freezing others. In ?this ?study, ?the ResNet-18 ?pre-trained CNNs model is used for feature ?extraction, followed by the support vector ?machine for multiple ?classes to classify medical images from multi-classes, which is ?used as the main ?classifier. Our ?proposed classification method ?was implemented on Kvasir and PH2 ?medical image ?datasets. The ?overall accuracy was 93.38% and ??91.67% for Kvasir and PH2 ?datasets, respectively. The ?classification ?results and performance ?of our proposed method outperformed some of the related ?similar ??methods in this area of study.? |
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Keywords |
Pre-trained convolution neural networks, medical image ?classification, support vector ?machine |
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Title |
Medical Image Classification using Pre-trained ??Convolutional Neural ?Networks and Support ?Vector ?Machine |
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Author |
Ali Ahmed |
| Citation |
Vol. 21 No. 6 pp. 1-6 |
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Abstract |
Recently, pre-trained convolutional neural network ?CNNs have been widely used ?and applied ?for medical image ?classification. These models can utilised in ?three different ways, ?for feature extraction, ?to use the architecture of ?the pre-trained ?model and to train some layers while freezing others. In ?this ?study, ?the ResNet-18 ?pre-trained CNNs model is used for feature ?extraction, followed by the support vector ?machine for multiple ?classes to classify medical images from multi-classes, which is ?used as the main ?classifier. Our ?proposed classification method ?was implemented on Kvasir and PH2 ?medical image ?datasets. The ?overall accuracy was 93.38% and ??91.67% for Kvasir and PH2 ?datasets, respectively. The ?classification ?results and performance ?of our proposed method outperformed some of the related ?similar ??methods in this area of study.? |
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Keywords |
Pre-trained convolution neural networks, medical image ?classification, support vector ?machine |
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Title |
Medical Image Classification using Pre-trained ??Convolutional Neural ?Networks and Support ?Vector ?Machine |
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Author |
Ali Ahmed |
| Citation |
Vol. 21 No. 6 pp. 1-6 |
|
Abstract |
Recently, pre-trained convolutional neural network ?CNNs have been widely used ?and applied ?for medical image ?classification. These models can utilised in ?three different ways, ?for feature extraction, ?to use the architecture of ?the pre-trained ?model and to train some layers while freezing others. In ?this ?study, ?the ResNet-18 ?pre-trained CNNs model is used for feature ?extraction, followed by the support vector ?machine for multiple ?classes to classify medical images from multi-classes, which is ?used as the main ?classifier. Our ?proposed classification method ?was implemented on Kvasir and PH2 ?medical image ?datasets. The ?overall accuracy was 93.38% and ??91.67% for Kvasir and PH2 ?datasets, respectively. The ?classification ?results and performance ?of our proposed method outperformed some of the related ?similar ??methods in this area of study.? |
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Keywords |
Pre-trained convolution neural networks, medical image ?classification, support vector ?machine |
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Title |
Medical Image Classification using Pre-trained ??Convolutional Neural ?Networks and Support ?Vector ?Machine |
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Author |
Ali Ahmed |
| Citation |
Vol. 21 No. 6 pp. 1-6 |
|
Abstract |
Recently, pre-trained convolutional neural network ?CNNs have been widely used ?and applied ?for medical image ?classification. These models can utilised in ?three different ways, ?for feature extraction, ?to use the architecture of ?the pre-trained ?model and to train some layers while freezing others. In ?this ?study, ?the ResNet-18 ?pre-trained CNNs model is used for feature ?extraction, followed by the support vector ?machine for multiple ?classes to classify medical images from multi-classes, which is ?used as the main ?classifier. Our ?proposed classification method ?was implemented on Kvasir and PH2 ?medical image ?datasets. The ?overall accuracy was 93.38% and ??91.67% for Kvasir and PH2 ?datasets, respectively. The ?classification ?results and performance ?of our proposed method outperformed some of the related ?similar ??methods in this area of study.? |
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Keywords |
Pre-trained convolution neural networks, medical image ?classification, support vector ?machine |
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Title |
Medical Image Classification using Pre-trained ??Convolutional Neural ?Networks and Support ?Vector ?Machine |
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Author |
Ali Ahmed |
| Citation |
Vol. 21 No. 6 pp. 1-6 |
|
Abstract |
Recently, pre-trained convolutional neural network ?CNNs have been widely used ?and applied ?for medical image ?classification. These models can utilised in ?three different ways, ?for feature extraction, ?to use the architecture of ?the pre-trained ?model and to train some layers while freezing others. In ?this ?study, ?the ResNet-18 ?pre-trained CNNs model is used for feature ?extraction, followed by the support vector ?machine for multiple ?classes to classify medical images from multi-classes, which is ?used as the main ?classifier. Our ?proposed classification method ?was implemented on Kvasir and PH2 ?medical image ?datasets. The ?overall accuracy was 93.38% and ??91.67% for Kvasir and PH2 ?datasets, respectively. The ?classification ?results and performance ?of our proposed method outperformed some of the related ?similar ??methods in this area of study.? |
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Keywords |
Pre-trained convolution neural networks, medical image ?classification, support vector ?machine |
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Title |
Medical Image Classification using Pre-trained ??Convolutional Neural ?Networks and Support ?Vector ?Machine |
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Author |
Ali Ahmed |
| Citation |
Vol. 21 No. 6 pp. 1-6 |
|
Abstract |
Recently, pre-trained convolutional neural network ?CNNs have been widely used ?and applied ?for medical image ?classification. These models can utilised in ?three different ways, ?for feature extraction, ?to use the architecture of ?the pre-trained ?model and to train some layers while freezing others. In ?this ?study, ?the ResNet-18 ?pre-trained CNNs model is used for feature ?extraction, followed by the support vector ?machine for multiple ?classes to classify medical images from multi-classes, which is ?used as the main ?classifier. Our ?proposed classification method ?was implemented on Kvasir and PH2 ?medical image ?datasets. The ?overall accuracy was 93.38% and ??91.67% for Kvasir and PH2 ?datasets, respectively. The ?classification ?results and performance ?of our proposed method outperformed some of the related ?similar ??methods in this area of study.? |
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Keywords |
Pre-trained convolution neural networks, medical image ?classification, support vector ?machine |
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URL |
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Title |
Medical Image Classification using Pre-trained ??Convolutional Neural ?Networks and Support ?Vector ?Machine |
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Author |
Ali Ahmed |
| Citation |
Vol. 21 No. 6 pp. 1-6 |
|
Abstract |
Recently, pre-trained convolutional neural network ?CNNs have been widely used ?and applied ?for medical image ?classification. These models can utilised in ?three different ways, ?for feature extraction, ?to use the architecture of ?the pre-trained ?model and to train some layers while freezing others. In ?this ?study, ?the ResNet-18 ?pre-trained CNNs model is used for feature ?extraction, followed by the support vector ?machine for multiple ?classes to classify medical images from multi-classes, which is ?used as the main ?classifier. Our ?proposed classification method ?was implemented on Kvasir and PH2 ?medical image ?datasets. The ?overall accuracy was 93.38% and ??91.67% for Kvasir and PH2 ?datasets, respectively. The ?classification ?results and performance ?of our proposed method outperformed some of the related ?similar ??methods in this area of study.? |
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Keywords |
Pre-trained convolution neural networks, medical image ?classification, support vector ?machine |
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URL |
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Title |
Medical Image Classification using Pre-trained ??Convolutional Neural ?Networks and Support ?Vector ?Machine |
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Author |
Ali Ahmed |
| Citation |
Vol. 21 No. 6 pp. 1-6 |
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Abstract |
Recently, pre-trained convolutional neural network ?CNNs have been widely used ?and applied ?for medical image ?classification. These models can utilised in ?three different ways, ?for feature extraction, ?to use the architecture of ?the pre-trained ?model and to train some layers while freezing others. In ?this ?study, ?the ResNet-18 ?pre-trained CNNs model is used for feature ?extraction, followed by the support vector ?machine for multiple ?classes to classify medical images from multi-classes, which is ?used as the main ?classifier. Our ?proposed classification method ?was implemented on Kvasir and PH2 ?medical image ?datasets. The ?overall accuracy was 93.38% and ??91.67% for Kvasir and PH2 ?datasets, respectively. The ?classification ?results and performance ?of our proposed method outperformed some of the related ?similar ??methods in this area of study.? |
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Keywords |
Pre-trained convolution neural networks, medical image ?classification, support vector ?machine |
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URL |
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Title |
Medical Image Classification using Pre-trained ??Convolutional Neural ?Networks and Support ?Vector ?Machine |
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Author |
Ali Ahmed |
| Citation |
Vol. 21 No. 6 pp. 1-6 |
|
Abstract |
Recently, pre-trained convolutional neural network ?CNNs have been widely used ?and applied ?for medical image ?classification. These models can utilised in ?three different ways, ?for feature extraction, ?to use the architecture of ?the pre-trained ?model and to train some layers while freezing others. In ?this ?study, ?the ResNet-18 ?pre-trained CNNs model is used for feature ?extraction, followed by the support vector ?machine for multiple ?classes to classify medical images from multi-classes, which is ?used as the main ?classifier. Our ?proposed classification method ?was implemented on Kvasir and PH2 ?medical image ?datasets. The ?overall accuracy was 93.38% and ??91.67% for Kvasir and PH2 ?datasets, respectively. The ?classification ?results and performance ?of our proposed method outperformed some of the related ?similar ??methods in this area of study.? |
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Keywords |
Pre-trained convolution neural networks, medical image ?classification, support vector ?machine |
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URL |
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Title |
Medical Image Classification using Pre-trained ??Convolutional Neural ?Networks and Support ?Vector ?Machine |
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Author |
Ali Ahmed |
| Citation |
Vol. 21 No. 6 pp. 1-6 |
|
Abstract |
Recently, pre-trained convolutional neural network ?CNNs have been widely used ?and applied ?for medical image ?classification. These models can utilised in ?three different ways, ?for feature extraction, ?to use the architecture of ?the pre-trained ?model and to train some layers while freezing others. In ?this ?study, ?the ResNet-18 ?pre-trained CNNs model is used for feature ?extraction, followed by the support vector ?machine for multiple ?classes to classify medical images from multi-classes, which is ?used as the main ?classifier. Our ?proposed classification method ?was implemented on Kvasir and PH2 ?medical image ?datasets. The ?overall accuracy was 93.38% and ??91.67% for Kvasir and PH2 ?datasets, respectively. The ?classification ?results and performance ?of our proposed method outperformed some of the related ?similar ??methods in this area of study.? |
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Keywords |
Pre-trained convolution neural networks, medical image ?classification, support vector ?machine |
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URL |
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Title |
Medical Image Classification using Pre-trained ??Convolutional Neural ?Networks and Support ?Vector ?Machine |
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Author |
Ali Ahmed |
| Citation |
Vol. 21 No. 6 pp. 1-7 |
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Abstract |
Recently, pre-trained convolutional neural network ?CNNs have been widely used ?and applied ?for medical image ?classification. These models can utilised in ?three different ways, ?for feature extraction, ?to use the architecture of ?the pre-trained ?model and to train some layers while freezing others. In ?this ?study, ?the ResNet-18 ?pre-trained CNNs model is used for feature ?extraction, followed by the support vector ?machine for multiple ?classes to classify medical images from multi-classes, which is ?used as the main ?classifier. Our ?proposed classification method ?was implemented on Kvasir and PH2 ?medical image ?datasets. The ?overall accuracy was 93.38% and ??91.67% for Kvasir and PH2 ?datasets, respectively. The ?classification ?results and performance ?of our proposed method outperformed some of the related ?similar ??methods in this area of study.? |
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Keywords |
Pre-trained convolution neural networks, medical image ?classification, support vector ?machine |
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URL |
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Title |
Medical Image Classification using Pre-trained ??Convolutional Neural ?Networks and Support ?Vector ?Machine |
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Author |
Ali Ahmed |
| Citation |
Vol. 21 No. 6 pp. 1-6 |
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Abstract |
Recently, pre-trained convolutional neural network ?CNNs have been widely used ?and applied ?for medical image ?classification. These models can utilised in ?three different ways, ?for feature extraction, ?to use the architecture of ?the pre-trained ?model and to train some layers while freezing others. In ?this ?study, ?the ResNet-18 ?pre-trained CNNs model is used for feature ?extraction, followed by the support vector ?machine for multiple ?classes to classify medical images from multi-classes, which is ?used as the main ?classifier. Our ?proposed classification method ?was implemented on Kvasir and PH2 ?medical image ?datasets. The ?overall accuracy was 93.38% and ??91.67% for Kvasir and PH2 ?datasets, respectively. The ?classification ?results and performance ?of our proposed method outperformed some of the related ?similar ??methods in this area of study.? |
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Keywords |
Pre-trained convolution neural networks, medical image ?classification, support vector ?machine |
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URL |
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Title |
Medical Image Classification using Pre-trained ??Convolutional Neural ?Networks and Support ?Vector ?Machine |
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Author |
Ali Ahmed |
| Citation |
Vol. 21 No. 6 pp. 1-6 |
|
Abstract |
Recently, pre-trained convolutional neural network ?CNNs have been widely used ?and applied ?for medical image ?classification. These models can utilised in ?three different ways, ?for feature extraction, ?to use the architecture of ?the pre-trained ?model and to train some layers while freezing others. In ?this ?study, ?the ResNet-18 ?pre-trained CNNs model is used for feature ?extraction, followed by the support vector ?machine for multiple ?classes to classify medical images from multi-classes, which is ?used as the main ?classifier. Our ?proposed classification method ?was implemented on Kvasir and PH2 ?medical image ?datasets. The ?overall accuracy was 93.38% and ??91.67% for Kvasir and PH2 ?datasets, respectively. The ?classification ?results and performance ?of our proposed method outperformed some of the related ?similar ??methods in this area of study.? |
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Keywords |
Pre-trained convolution neural networks, medical image ?classification, support vector ?machine |
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URL |
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Title |
Medical Image Classification using Pre-trained ??Convolutional Neural ?Networks and Support ?Vector ?Machine |
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Author |
Ali Ahmed |
| Citation |
Vol. 21 No. 6 pp. 1-6 |
|
Abstract |
Recently, pre-trained convolutional neural network ?CNNs have been widely used ?and applied ?for medical image ?classification. These models can utilised in ?three different ways, ?for feature extraction, ?to use the architecture of ?the pre-trained ?model and to train some layers while freezing others. In ?this ?study, ?the ResNet-18 ?pre-trained CNNs model is used for feature ?extraction, followed by the support vector ?machine for multiple ?classes to classify medical images from multi-classes, which is ?used as the main ?classifier. Our ?proposed classification method ?was implemented on Kvasir and PH2 ?medical image ?datasets. The ?overall accuracy was 93.38% and ??91.67% for Kvasir and PH2 ?datasets, respectively. The ?classification ?results and performance ?of our proposed method outperformed some of the related ?similar ??methods in this area of study.? |
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Keywords |
Pre-trained convolution neural networks, medical image ?classification, support vector ?machine |
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