Neutrosophic Sets and Systems
Abstract
Medical image processing has become a critical research area due to the vast amounts of digital image data available. However, medical images often suffer from poor illumination and low visibility of significant structures, requiring image enhancement to improve image quality before processing. In this paper, we propose a technique for enhancing medical images by removing noise and improving contrast based on three different enhancing transforms. The proposed technique embeds the image into a neutrosophic fuzzy domain, where it is mapped into three different levels of trueness, falseness, and indeterminacy, and each level is processed individually using the enhancement transforms. We compare the proposed technique with four other systems for leukemia detection and classification using accuracy and T, I, and F values. The proposed system performs the best with an accuracy of 98%, outperforming the other systems in terms of accuracy, degree of indeterminacy, and falsity. The proposed system uses different algorithms and filters to process images and extract features like color and texture. The system's classification uses k-means for segmentation and SVM for classification. The paper highlights the importance of considering T, I, and F values in evaluating the performance of different systems for leukemia detection and classification, providing a more accurate representation of the uncertainty and ambiguity involved in the evaluation process.
Recommended Citation
Salama, A. A.; Mahmoud Y. Shams; Huda E. Khalid; and Doaa E. Mousa. "Enhancing Medical Image Quality using Neutrosophic Fuzzy Domain and Multi-Level Enhancement Transforms: A Comparative Study for Leukemia Detection and Classification." Neutrosophic Sets and Systems 65, 1 (2024). https://digitalrepository.unm.edu/nss_journal/vol65/iss1/3
