Abstract
A prevalent form of cancer that affects millions of individuals globally is skin cancer. The visual examination of skin lesions, however, is a challenging and time-consuming procedure that calls for the knowledge of dermatologists. The proposed effort intends to create an accurate, feasible and effective system for detecting skin lesions that can help dermatologists identify and treat a variety of skin conditions. To extract features from skin lesion photos, the method uses a pre-trained Convolutional Neural Network (CNN). These characteristics are then fed into a Recurrent Neural Network (RNN) for temporal modelling. The early diagnosis of numerous skin illnesses depends greatly on the detection of skin lesions. Deep learning models, particularly CNNs, have demonstrated impressive performance in the computer-aided diagnosis of skin lesions in recent years. This work uses the HAM 10000 dataset to suggest a hybrid CNN and RNN model for skin lesion detection.
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