Skin cancer classification enabled mobile neuro fuzzy network and entropy with weber local binary pattern based for feature extraction

Abstract

Skin cancer (SC) is the most quickly spreading diagnosis all over the world due to limited resources presented. Accurately diagnosing skin lesions in their early stages could help clinicians make better decisions by raising the likelihood of a cure before cancer develops. However, because to the imbalance and scarcity of most skin illness images used for training, it is challenging to achieve automatic skin cancer classification. Also, it experiences difficulties like high consumption of time. Deep learning (DL) modules are introduced to deal with these difficulties as well as it aids dermatologists in creating precise examinations. To conquer this concern, a novel entropy system for SC classification is developed by a hybrid network named as Mobile Neuro Fuzzy Network (MNFN), where MNFN is the fusion of MobileNet and Cascade Neuro fuzzy Network (cascade NFN). The input image is forwarded to the pre-processing unit, which is done by using a bilateral filter. Skin lesion segmentation is done by Squeeze U-SegNet. Feature extraction is performed using the proposed Weber Local Binary Pattern (WLBP) with entropy is also extracted. Then, data augmentation (DA) by oversampling scheme Synthetic Minority Oversampling Technique (SMOTE) is performed. Finally, SC classification is executed by employing a hybrid model MNFN. Here the SC is classified into various class labels. The analytic metrics used for MNFN namely, accuracy, True Positive Rate (TPR), True Negative Rate (TNR), False Positive Rate(FPR) and False Negative Rate (FNR) gained suitable values of 0.930, 0.945, 0.931, 0.110 and 0.094.