Improved Fuzzy Based Segmentation with Hybrid Classification for Skin Disease Detection

Abstract

In this paper, we present a novel approach for the early detection of skin disorders, aiming to improve the prediction accuracy and precision. Our motivation lies in addressing the challenges posed by the uncertainty in skin lesion features. To achieve this, we employ fuzzy-based segmentation techniques to enhance the quality of skin disorder image processing. Additionally, we explore the integration of intensity data to further refine the segmented images. Our methodology consists of three main steps: segmentation, feature extraction, and classification. We begin by inputting the image into the segmentation phase, followed by the application of a fuzzy c-means clustering process to improve the segmentation output. From the resulting segmented image, we extract features using local binary pattern and local Gabor XOR pattern techniques. These features are then subjected to classification, employing a hybrid classifier that combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. The use of this hybrid classifier is particularly significant, as it leverages the strengths of both CNN and LSTM to enhance the predictive power of our model. Through rigorous experimentation, we have achieved promising results. Our proposed method exhibits an impressive accuracy of 94.6% and precision of 95.5% in comparison to the Conventional FCM Model, which only achieves an accuracy of 87% and precision of 82.5%.