A Deep Learning-Based Model for Melanoma Detection in Both Dermoscopic and Digital Images

Although Melanoma has been classified as the deadliest kind of skin cancer, with an early prognosis, the chance of treatment goes up. To identify and detect melanoma in digital and dermoscopic images, a deep learning-based model that uses Inception-v3 architecture has been developed in this study. The Hebbian principle and the multi-scale processing method were both used in the architectural design of Inception-v3 to guarantee excellent optimization. This system employs parallel computation throughout many GPUs to use RMSprop as an optimizer. Network weights were fine-tuned throughout the training phase of the model, which feeds mistakes from each iteration back into the Inception-v3 network via the backpropagation approach. Upon finishing the training phase, the model will use the lesion image as input to the pipeline to predict a mole's diagnosis. The PH2 dataset contains 200 dermoscopic images, from which the model achieves accuracy, specificity, and sensitivity of 88.55%, 86.94%, and 95.00%, respectively. Additionally, the model's accuracy of 83.49% was assessed using a digital dataset of 170 photos from UMCG dataset. In both cases, the model is considered as a binary classifier and evaluated using a five-fold cross-validation approach.