Use of Xception Architecture for the Classification of Skin Lesions
This study investigates the application of the Xception architecture for accurate classification of skin lesions, focusing on the early detection of melanoma and other malignant skin conditions. Utilizing deep learning techniques, the research aims to enhance the precision and efficiency of skin les...
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| Format: | Article |
| Language: | English |
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International Institute of Informatics and Cybernetics
2024-06-01
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| Series: | Journal of Systemics, Cybernetics and Informatics |
| Subjects: | |
| Online Access: | http://www.iiisci.org/Journal/PDV/sci/pdfs/ZA541NL24.pdf
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| _version_ | 1850178334924734464 |
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| author | Cledmir Tejada Gustavo Espinoza Daniel Subauste |
| author_facet | Cledmir Tejada Gustavo Espinoza Daniel Subauste |
| author_sort | Cledmir Tejada |
| collection | DOAJ |
| description | This study investigates the application of the Xception architecture for accurate classification of skin lesions, focusing on the early detection of melanoma and other malignant skin conditions. Utilizing deep learning techniques, the research aims to enhance the precision and efficiency of skin lesions diagnosis. The study utilizes the TensorFlow framework and the HAM10000 dataset, comprising a vast collection of benign and malignant skin lesion images, for training and evaluating the Xception model. Preprocessing steps, including data splitting, augmentation, and image resizing, are applied to the dataset. The Xception architecture, a deep convolutional neural network, serves as the foundational model, supplemented with customized classification layers for specialized features and predictions. The model's performance is evaluated using diverse metrics. The experimental outcomes reveal the Xception architecture's potential in accurately classifying skin lesions. Moreover, the study underscores the significance of extensive and diverse datasets, as well as rigorous clinical validation, in the development of deep learning models for skin cancer detection. The findings contribute to the advancement of early melanoma detection, thereby improving patient outcomes and alleviating the burden of the disease. |
| format | Article |
| id | doaj-art-5b7ca2175a974eb09e7b88ab25b92f15 |
| institution | OA Journals |
| issn | 1690-4524 |
| language | English |
| publishDate | 2024-06-01 |
| publisher | International Institute of Informatics and Cybernetics |
| record_format | Article |
| series | Journal of Systemics, Cybernetics and Informatics |
| spelling | doaj-art-5b7ca2175a974eb09e7b88ab25b92f152025-08-20T02:18:46ZengInternational Institute of Informatics and CyberneticsJournal of Systemics, Cybernetics and Informatics1690-45242024-06-012232025Use of Xception Architecture for the Classification of Skin LesionsCledmir TejadaGustavo EspinozaDaniel SubausteThis study investigates the application of the Xception architecture for accurate classification of skin lesions, focusing on the early detection of melanoma and other malignant skin conditions. Utilizing deep learning techniques, the research aims to enhance the precision and efficiency of skin lesions diagnosis. The study utilizes the TensorFlow framework and the HAM10000 dataset, comprising a vast collection of benign and malignant skin lesion images, for training and evaluating the Xception model. Preprocessing steps, including data splitting, augmentation, and image resizing, are applied to the dataset. The Xception architecture, a deep convolutional neural network, serves as the foundational model, supplemented with customized classification layers for specialized features and predictions. The model's performance is evaluated using diverse metrics. The experimental outcomes reveal the Xception architecture's potential in accurately classifying skin lesions. Moreover, the study underscores the significance of extensive and diverse datasets, as well as rigorous clinical validation, in the development of deep learning models for skin cancer detection. The findings contribute to the advancement of early melanoma detection, thereby improving patient outcomes and alleviating the burden of the disease.http://www.iiisci.org/Journal/PDV/sci/pdfs/ZA541NL24.pdf skin cancerdeep learningskin lesionscnn ar ch itecture |
| spellingShingle | Cledmir Tejada Gustavo Espinoza Daniel Subauste Use of Xception Architecture for the Classification of Skin Lesions Journal of Systemics, Cybernetics and Informatics skin cancer deep learning skin lesions cnn ar ch itecture |
| title | Use of Xception Architecture for the Classification of Skin Lesions |
| title_full | Use of Xception Architecture for the Classification of Skin Lesions |
| title_fullStr | Use of Xception Architecture for the Classification of Skin Lesions |
| title_full_unstemmed | Use of Xception Architecture for the Classification of Skin Lesions |
| title_short | Use of Xception Architecture for the Classification of Skin Lesions |
| title_sort | use of xception architecture for the classification of skin lesions |
| topic | skin cancer deep learning skin lesions cnn ar ch itecture |
| url | http://www.iiisci.org/Journal/PDV/sci/pdfs/ZA541NL24.pdf
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| work_keys_str_mv | AT cledmirtejada useofxceptionarchitecturefortheclassificationofskinlesions AT gustavoespinoza useofxceptionarchitecturefortheclassificationofskinlesions AT danielsubauste useofxceptionarchitecturefortheclassificationofskinlesions |