Detection and segmentation of melanoma skin cancer in dermoscopy images using modified Alexnet convolutional neural network-morphological methodology

Melanoma is most vicious and dangerous type of skin cancer that its early detection can save patients' lives. Computer-aided methods can be used for this early detection with acceptable performance. In this article, the melanoma skin cancer dermoscopy images are detected and cancer regions are segmented using the proposed modified AlexNet convolutional neural network-morphological segmentation (CNN-MS) method. This proposed work consists of enhancement stage, feature matrix construction stage, CNN classification stage, and segmentation stage. In enhancement stage, the skin pixels are contrast enhanced by histogram equalization technique and then non-sub sampled contourlet transform (NSCT) is applied on the enhanced skin image to obtain the decomposed coefficients. Further, local ternary pattern (LTP) features are computed from the enhanced image and the feature matrix (FM) is constructed by integrating the NSCT coefficients and the LTP features. This constructed FM is fed to the CNN model for the classification of Melanoma skin cancer images from the normal skin images. Finally, morphological functions are applied on the classified melanoma skin image to segment the cancer regions. The performance of this developed model for skin cancer detection is analyzed on international skin imaging collaboration (ISIC) dataset images and achieves 98.11% of Se, 98.5% of Sp, 99.11% of Acc, and 0.96 of MCC. Also, the performance of this developed model for skin cancer detection is analyzed on SIIM-ISIC dataset images and achieves 98.04% Se, 98.46% Sp, 99.12% Acc, and 0.97 MCC.