Cascaded Approach for Image Segmentation and Classification for Skin Cancer Detection

With advancements in medical sciences, the detection and classification of skin cancer in patients is a field where extensive efforts are been put in by researchers to improve real-time solutions. It is a state where the growth of skin cells goes out of control. Since cancer evolves with respect to time, hence, to classify the type of skin cancer we need to segment the image properly before classification, which can lead to more reliable results in the detection of the type of cancer that has occurred. In this paper, we have proposed a cascaded approach to detect the type of skin cancer where instead of directly feeding an input as an input for classification, we first input the original image to U-net for segmentation and derive the granular details from it. Followed by which, the segmented image is fed and forwarded to CNNs through which the classification of the cancer is performed. We have implemented and compared the performances of MobileNet, DenseNet121, and ResNet50 in the classification of skin cancer disease.