Diagnosing skin cancer via C-means segmentation with enhanced fuzzy optimization

Abstract

The early detection of cancer decreases the death rate as well, but mostly the disorder symptoms are unpredictable. Numerous skin cancer detection techniques are available in the dice, yet the effectiveness remains unachieved. This paper aims to introduce a skin cancer detection technique that characterizes the nature of cancer: normal, benign or malignant. The proposed technique includes three stages like Segmentation, Feature Extraction, and Classification. Here, the Fuzzy C-means Clustering (FCM) is used to segment the given input image. Then, the features are mined from the segmented image using Local Vector Pattern (LVP) and Local Binary Pattern (LBP). Subsequently, the Fuzzy classifier is used to do the classification process that gets the extracted features (LVP+LBP) as the input. The classifier outputs the nature of the image. As the primary contribution of this work, the limits of membership functions in the Fuzzy classifier are optimally selected by a new improved Rider Optimization Algorithm (ROA) termed as Distance Oriented ROA (DOROA). The performance of the proposed DOROA model is compared over other conventional models in terms of accuracy, sensitivity, specificity, precision, Negative Predictive Value (NPV), F1-score and Matthews correlation coefficient (MCC), False positive rate (FPR), False Negative Rate (FNR), and False Discovery Rate (FDR).