Hybrid fuzzy-based Shepard convolutional maxout network-based skin cancer detection

Skin cancer arises from the uncontrolled growth of skin cells. Timely detection can significantly lower mortality rates and improve patient outcomes. However, diagnosing melanoma can be challenging due to its similarity to benign lesions. This study introduces a fuzzy Shepard convolutional neural network (FSCMN) for detecting skin cancer in images. Initially, the pre-processing is done by using a bilateral filter. Then, the skin lesions are segmented by using recurrent prototypical network (RP-Net). Next, features are extracted using convolutional neural networks (CNN), local vector pattern (LVP), grey level co-occurrence matrix (GLCM) texture features, and entropy-based local directional texture pattern (LDTP). Finally, skin cancer detection is performed by proposed FSCMN, which integrates fuzzy logic, Shepard convolutional neural network (ShCNN), and deep maxout network (DMN). The FSCMN approach achieved impressive results, with accuracy, true positive rate (TPR), and true negative rate (TNR) of 0.928, 0.939, and 0.914.