A custom CNN is trained on a manually filtered subset of HAM10000 to classify benign versus malignant skin lesions, with no evidence for the interpretability method promised in the abstract.
4 PROPOSED METHODOLOGY In this part, we outline the subsequent phases and our methodology's flowchart, which is seen in Fig
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Using Computer Vision for Skin Disease Diagnosis in Bangladesh Enhancing Interpretability and Transparency in Deep Learning Models for Skin Cancer Classification
A custom CNN is trained on a manually filtered subset of HAM10000 to classify benign versus malignant skin lesions, with no evidence for the interpretability method promised in the abstract.