A Review on Artificial Intelligence Based Heuristic Models for Brain Tumor Image Classification and Segmentation - Sai Prasad, M; Khan, Nafis Uddin; Kumar P, Pramod (Informatica, 2025)
Even with the tremendous advancements in medical technology, the most laborious and complex work that doctors still have to do is segment tumors. Radiologists most commonly employ magnetic resonance imaging (MRI) to examine interior human body parts without dissecting them, although manual inspection is time-consuming and wastes valuable work hours. Since it might lead to early diagnosis, effective automated sorting of brain cancers from MRI images is crucial, reduce errors in work hours, propagate automation in tumor removal, and aid in treatment decision-making. Computer-aided image analysis can also be a potential solution for early disease detection, such as cancer or tumors. This paper seeks to emphasize the strategies in light of these challenges. For physicians, identifying tumors in the brain is still a highly challenging and lengthy procedure. despite the tremendous advancements in medical technology. Early and comprehensive brain tumor detection may result in higher survival rates since it enables effective and efficient treatment. Enhanced efficiency and consistent precision could come from the computerized recognition and categorization of brain tumors. However, it is well recognized that the strategy and picture modality have an impact on the accuracy performance of automatic detection and classification techniques. The latest detection methods are reviewed in this work along with their benefits and drawbacks.