Deep Learning Techniques for Skin Lesion Identification and Categorization: Enhancing Diagnostic Precision and Treatment Outcomes

Skin lesions, ranging from benign to malignant forms, are crucial indicators of dermatological health, with early detection being paramount for effective treatment and improved prognosis. The inherent complexity in visual characteristics, including variations in texture, color, and shape, complicates manual diagnosis, making automation a promising solution. This study systematically compares the performance of four advanced DL models- Convolutional Neural Networks, VGG16, DenseNet, and InceptionV3- for classification of skin lesions utilizing the HAM10000 dataset, which comprises over 10,0015 dermoscopic images across seven distinct lesion categories. The models were rigorously evaluated by considering Accuracy(acc), Precision(pre), Recall(re), and F1-Score(F1). The CNN baseline model achieved an acc of 97.8%, while the VGG16 model yielded 96.5%. The InceptionV3 model demonstrated enhanced feature learning with an acc of 96.8%, and the DN model achieved an acc of 97.5%. These results emphasize the significant potential of DL in automating skin lesion classification, offering robust diagnostic support for clinicians and facilitating early detection of skin cancers.