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).This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Processing of images plays a vital role in many fields such as medical and scientific applications. During the transmission of images, effect of noise plays a key role. A fuzzy filter is presented for additive noise removal from color images. During the process of noise removal, some of the edges may be disappeared. This paper presents two independent fuzzy based edge linking algorithms which are capable of finding a set of edge points in an image and linking these edge points by thresholding. The first algorithm includes a set of 16 fuzzy templates, representing the edge profiles of different types. The second algorithm relies on the image gradient to locate breaks in uniform regions and is based on fuzzy if-then rules. Performance evaluation of these algorithms is known by calculating peak signal to noise ratio (PSNR).

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Most of images like medical images, satellite images and even real life photographs may suffer from poor contrast due to the inadequate or insufficient lighting during image acquiring. So there is a necessity of contrast enhancement of images. In this paper three enhancement techniques namely fuzzy rule based contrast enhancement, contrast enhancement using intensification (INT) operator, and contrast enhancement using fuzzy expected value (FEV) are presented for the low contrast grayscale images. Comparative analysis of these enhancement techniques is carried out by means of index of fuzziness (IOF) and processing time.