• P. A. Monisha
• Dr. S. Sukumaran

Medical images, such as X-rays, mammograms, and magnetic resonance imaging (MRI), play a crucial role in the healthcare sector by assisting doctors in the early detection and accurate diagnosis of illnesses. However, medical images suffer from a number of problems throughout the acquisition and processing phases, such as noise, poor contrast, and low-light exposure, among others. Cancer, a disease with numerous subtypes, poses a deadly threat to human life, with the potential for successful clinical treatment heavily reliant on early detection and appropriate treatment planning. The classification of cancer patients into either low or high-risk subgroups is critical. Consequently, various research teams spanning the biomedical and bioinformatics fields have explored the use of Deep Learning (DL) technology in this crucial domain. These issues hinder the ability to extract useful information from the images and worsen the overall quality of the images. In order to provide patients with the best care possible, it is crucial to improve image quality by eliminating noise and increasing contrast. The use of appropriate image enhancement techniques improves picture properties such as contrast, visual quality, brightness, and entropy, making medical images more applicable to a range of uses. When faced with ambiguity, fuzzy logic is an effective technique. The most effective method for obtaining high-quality photographs is to use fuzzy logic during the image enhancing process. Fuzzy logic-based image enhancing techniques were used to MRI brain pictures in our study report. By comparing the performance of various methods Fuzzy Histogram Equalization (FHE), Adaptive Contrast Enhancement Algorithm (ACEA), Watershed (WS), and Ensemble Deep Neural Support Vector Machine (EDN-SVM) EDN-SVM is found to deliver the best results for noise removal in brain tumor images, offering excellent visual quality with enhanced contrast and richer image details.

fuzzy histogram equalization (FHE), Brain Tumor image, Watershed, Adaptive contrast enhancement algorithm (ACEA), Ensemble deep neural support vector machine (EDN-SVM).

"COMPARATIVE STUDY ON NOISE REMOVAL ALGORITHMS FOR TUMOR DETECTION IN BRAIN IMAGES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 7, page no.d755-d760, July-2025, Available :http://www.jetir.org/papers/JETIR2507387.pdf

"COMPARATIVE STUDY ON NOISE REMOVAL ALGORITHMS FOR TUMOR DETECTION IN BRAIN IMAGES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 7, page no. ppd755-d760, July-2025, Available at : http://www.jetir.org/papers/JETIR2507387.pdf