Skin Cancer Detection Using Deep Block Convolutional Neural Networks

One of the most common and potentially fatal forms of cancer, skin cancer is on the increase across the world. The likelihood of a positive outcome from therapy increases dramatically with early and precise diagnosis. This is especially true with deep learning approaches. This research introduces the use of Deep Block Convolutional Neural Networks (DB-CNNs) as a unique method for detecting skin cancer. The suggested DBCNN model was trained and tested using a large dataset consisting of photos of various skin lesions. DB-CNNs are built to take advantage of the hierarchical characteristics of skin lesions, capturing the complex patterns and textures that are essential to a correct diagnosis. The model makes use of a chain of convolutional layers, supplemented with blocks trained to extract features and establish spatial relationships. By combining these modules, the network can better identify nuances in skin lesion features, leading to more precise diagnoses. This study explores several data preparation techniques, such as image enhancement and normalization, to determine whether any of them may strengthen the model's robustness and generalizability.