International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | A Systematic Review of Brain Tumor Detection and Segmentation Methods in MRI Imaging |
|---|---|
| ABSTRACT | Brain tumor detection and segmentation from Magnetic Resonance Imaging (MRI) play a critical role in early diagnosis, treatment planning, and patient prognosis. Over the years, numerous techniques have been developed to automate this process, ranging from traditional image processing methods to advanced machine learning and deep learning approaches. This paper presents a systematic review of various brain tumor detection and segmentation techniques applied to MRI images, highlighting their methodologies, advantages, and limitations. The review begins with classical image processing techniques, including preprocessing, thresholding, edge detection, and morphological operations, that provide simple, computationally efficient solutions. It then explores machine learning methods that rely on feature extraction and classification algorithms, such as Support Vector Machines (SVMs) and K-Nearest Neighbors (KNNs). Furthermore, the paper examines recent advancements in deep learning, particularly Convolutional Neural Networks (CNN) and U-Net architectures, which have demonstrated superior performance in handling complex medical imaging tasks. A comparative analysis of these approaches is presented based on parameters such as accuracy, computational complexity, dataset requirements, and robustness to noise and intensity variations. The review also discusses key challenges in brain tumor detection, including variability in MRI data, similarity between tumor and normal tissues, and the need for large annotated datasets. |
| AUTHOR | ARYAN SHARMA, RENU P.G. Student, Department of CSE, Sat Kabir Institute of Technology and Management, Ladrawan, Haryana, India Assistant Professor, CSE, Sat Kabir Institute of Technology and Management, Ladrawan, Haryana, India |
| VOLUME | 184 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1405021 |
| pdf/21_A Systematic Review of Brain Tumor Detection and Segmentation Methods in MRI Imaging.pdf | |
| KEYWORDS | |
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