Study of Melanoma Detection and Classification Techniques

Melanoma is a type of skin cancer that starts and evolves from the pigment-producing cells known as melanocytes. There has been quite some research done in the area of melanoma classification through image detection and classification using machine learning- specifically deep learning and neural networks. Researchers have used CNN-Convolutional Neural Networks, DNN-Deep Neural Networks, some have even used RNN- Recurrent Neural Network and transfer Learning. The Research work has not been up-to the mark as of yet, we know this because there has been no news of models being put to clinical testing. Another setback to the process of creating a perfect algorithm and mode is the lack of data regarding melanoma, the largest dataset publicly available is the one provided by ISCI for its 2020 competitions it has 25333 as training data and about 8240 as testing image datasets, but the issue with these is that the do not contain just the images and data for melanoma, the are a dataset for 7 different skin lesions to be detected and classified. In this review/survey paper we will be reviewing the research work done in the past couple of years on the topic of melanoma detection and classification using Deep learning.