Early detection of melanoma skin cancer: A hybrid approach using fuzzy C-means clustering and differential evolution-based convolutional neural network

Abstract

Skin cancer is a prevalent type of disease that is challenging to predict, and early detection is crucial for successful treatment. In this study, we propose an improved strategy for early detection of three types of skin cancers using medical imaging. Our approach uses fuzzy C-means clustering for image segmentation, along with various filters and image features including Local Binary Pattern (LBP), RGB color-space, and GLCM methods. We also employ a Convolutional neural network (CNN) trained with differential evolution (DE) algorithm for classification. We evaluate the proposed technique using skin cancer image datasets HAM10000, and demonstrate its superior performance compared to traditional classifiers. Our approach achieves a detection accuracy of 91 %, which is significantly higher than other traditional methods in the same domain. To enhance the accuracy of skin cancer detection in medical imaging, the proposed technique can be automated using electronic devices like mobile phones.