Skin Cancer Detection Using A Multi-Scale AI Deep Learning Approach

Skin cancer occurrences are increasing, and melanoma is the deadliest type of the disease. Preventing the illness from spreading to other regions of the body requires early identification. In this work, an automated technique for identifying skin cancer without the need for physician assistance is presented. The suggested method for detecting melanoma lesions makes use of transfer learning. To reduce lighting effects and noise, the acquired photos are first pre-processed. By creating more training examples, image augmentation—a crucial deep learning technique—is then used to improve the dataset and raise the training process’ efficacy. Then, using transfer learning, a Convolutional Neural Network (CNN) is built using pre-learnt weights from the ImageNet dataset. The network is then adjusted to prioritise the detection of melanoma and differentiate it from benign tumours. Each of the three main phases in the method's processing of image data focusses on the central area of the picture where the suspicious lesion is located. The results from these processes are aggregated and used to train a fully connected neural network. The suggested solution outperformed other cutting-edge techniques, as seen by the 90.09% accuracy attained in testing on a collection of photos. Through concentrating on the questionable skin region at different phases, the method offers a more precise and trustworthy diagnosis.