Abstract
Basal cell carcinoma (BCC) is the most common type of skin cancer, accounting for approximately 80% of all cases. Early and accurate detection of BCC is essential for effective treatment and prevention of serious complications. In this work, a CNN (Convolutional Neural Networks) model is presented for the detection and classification of Basal Cell Carcinoma from dermoscopic cases. To create the proposed model, the HAM10000 dataset was used, which includes a set of images of dermatological lesions. Extensive experiments were performed to evaluate the accuracy of the model, as well as a comparison with the GRU and LSTM models. The results obtained indicate an accuracy of 93.5%, demonstrating that the proposed model has the ability to effectively identify and differentiate benign and malignant basal cell carcinoma lesions.
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