Skin Cancer Detection Using A Multi-Scale AI Deep Learning CNN Techniques

Skin cancer, of which melanoma is the deadliest type, is becoming more common. To stop it from spreading to other areas of the body, early detection is essential. This work reduces the need for medical intervention by introducing an automated method for detecting skin cancer. To detect melanoma lesions, the suggested approach uses transfer learning. The obtained photos are first pre-processed to minimize noise and illumination fluctuations. Image augmentation, a crucial deep learning approach, is used to improve the dataset and increase the training efficiency by producing more training samples. In order to create a Convolutional Neural Network (CNN), transfer learning is then applied by utilizing pre-trained weights using the ImageNet dataset. The network is optimized to detect melanoma and differentiate the item from benign tumors. The three main stages of the methodology focus on examining the image’s center, which is thought to be the location of the suspected lesion. A neural network that is entirely linked is trained using the combined outputs of these stages. On a test dataset, the suggested approach outperformed state-of-the-art methods, attaining an accuracy of 90.09%. This method offers a more precise and trustworthy diagnosis by focusing on the questionable skin area at several stages.