Developing an Efficient Deep Learning Model for the Classification of Skin Lesions

Abstract

This paper proposes a deep learning technique for classifying dermoscopic skin lesion pictures, improving diagnostic precision and clinical decision-making. The model extracts features using ResNet101V2 and infers using Monte Carlo dropout to determine prediction certainty. Grad-CAM additionally shows the model's output-affecting regions. This makes it easy to assess whether the network is targeting therapeutically relevant structures. A modular software system tracked preprocessing, training, and assessment to ensure approach clarity. Repeating trials and making systematic model adjustments is easy with this framework. We processed ISIC using defined metadata and stratified splitting. This was balanced and simple to comprehend how the model operated. Due to a lack of representative samples and an evident class imbalance, the framework performed well for the majority class but could not identify melanoma. Use uncertainty estimate and visual explanation combined to assess model reliability and identify ambiguous predictions. The development of dermatology-focused AI systems that are clinically interpretable and dependable is supported by these components.

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