International Journal of Science, Engineering and TechnologyAuthors: Jyoti Gahora, Bhanu Pratap Singh
Abstract: Brain tumor detection and classification are among the most critical challenges in medical image analysis due to the complexity and variability of tumor structures in Magnetic Resonance Imaging (MRI) scans. Early and accurate diagnosis is essential for effective treatment planning and improving patient survival rates. In recent years, deep learning techniques have significantly transformed the field of medical imaging by providing automated, efficient, and highly accurate diagnostic systems. This review paper presents a comprehensive analysis of recent advancements in machine learning and deep learning approaches for brain tumor detection and classification using MRI images. The study examines various Convolutional Neural Network (CNN) architectures, transfer learning models, attention mechanisms, hybrid frameworks, explainable artificial intelligence (XAI), and IoT-enabled healthcare systems. Additionally, the paper discusses preprocessing methods, segmentation techniques, classification strategies, and performance evaluation metrics used in recent research. The review also identifies major challenges such as limited annotated datasets, computational complexity, overfitting, lack of interpretability, and generalization issues across medical datasets. Finally, the paper highlights emerging trends and future research directions, including lightweight deep learning models, federated learning, multimodal imaging integration, and real-time clinical deployment. This review provides researchers and healthcare professionals with a detailed understanding of state-of-the-art deep learning techniques for intelligent brain tumor diagnosis systems.
