Fuzzy Cognitive Map and Deep-Seg Net for Enhancing Skin Cancer Segmentation and Detection

Global health challenges such as skin cancer, which arises from uncontrolled cell growth, pose a significant threat. The most prevalent form of skin cancer manifests in cells throughout the body, notably in the surface layer. Because of its widespread nature, early detection is urgently needed. An innovative approach is developed to segment and detect skin cancer by integrating Fuzzy Cognitive Maps (FCM) with Deep-Seg Nets. Adaptive bilateral filtering, preprocessing, and optimizing input skin cancer images are the first steps. In the following step, DeepSegNet, a hybrid of DeepJoint and SegNet, is used to segment the images. The segmented image is then enhanced using enhancement techniques, followed by extraction of diverse features such as texture, statistical attributes, Local Neighborhood Difference Patterns (LNDPs), and a novel contribution, a discrete wavelet transform (DWT)-based Local Directional Pattern. In skin cancer detection, FCM shows noteworthy proficiency. As a result of the evaluation of the proposed methodology, DeepSegNet scored 0.920 for dice coefficient, while the DeepSegNet+FCM model scored 97% accurate, 93% negative predictive value, 94% positive predictive value, 98% sensitivity, and 96% specificity. The combination of FCM and Deep-Seg Net offers a potential path toward more accurate and efficient skin cancer diagnosis.