Explainable AI framework for skin cancer classification, and melanoma segmentation
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Full Text |
Pdf
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Author |
K. Srilatha and N. Satheesh Kumar
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e-ISSN |
1819-6608 |
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On Pages
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1012-1025
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Volume No. |
19
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Issue No. |
15
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Issue Date |
October 31, 2024
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DOI |
https://doi.org/10.59018/082432
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Keywords |
skin cancer detection, deep learning, melanoma segmentation, explainable AI, convolutional neural networks (CNNs).
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Abstract
Identification and classification of skin diseases are two critical challenges faced in diagnosing and treating patients suffering from them. Deep learning models have been created to best identify and classify skin problems to detect and identify them correctly and effectively. This paper proposes a comprehensive framework for accurate skin cancer prediction, classification, and melanoma surgical lesion extraction. Primarily, a comprehensive extraction method leveraging the unique approach of DenseNet201 and the Local Interpretable Model-Agnostic Explanation offers accurate insight into model decision making and prediction. Secondly, the model has a mid-extraction phase that utilizes advanced convolutional neural network levels to detect the boundaries of melanoma lesions correctly. The framework results in terms of IOU, accuracy, precision, recall, and other metrics compared to existing models like FPN, MAN, and U-Net. The framework presented in our model is smart, easy to use, and can provide functional and accurate information, which means it can be used in clinical practice.
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