Analysis of Skin Lesions for Identifying of Skin Cancer Melanoma using Customized CNN Model with Transfer Learning

Melanoma is a type of cancer known to be the most dangerous and is called the life threatening disease. Every year, there is rise in number of people being diagnosed with melanoma which is more frequent among the males than the females. This type of skin cancer has an uncontrollable growth of cells that can grow really faster, if not treated it can spread all over the body. Hence this disease needs to be detected at the early stages so that the patients can be treated properly. Machine learning approaches were applied to detect skin cancer but the drawbacks were that the performance to identify the cancer was limited. Therefore, we constructed a model by utilizing a Convolution Neural Network of the most advanced deep learning where we conduct investigations for discovering the melanoma skin cancer on the basis of the skin lesion. In this research, using transfer learning techniques ResNet and VGG based models have been brought together for melanoma skin detection. The procedures were applied to train these models using optimizers like RMSprop, Adam, Stochastic Gradient Descent (SGD) and findings were presented with the support of accuracy, confusion matrix, sensitivity, specificity and precision.