article · IEEE Access
Early detection of brain tumours from magnetic resonance imaging is crucial for timely medical treatment, yet manual examination by specialists remains complex and time-consuming. An automated diagnostic framework has been developed to improve both tumour segmentation and classification across multiple tumour categories, including meningioma and pituitary tumours. The process begins with image enhancement through contrast limited adaptive histogram equalisation and diffusion filtering. Next, fuzzy c-means clustering isolates the tumour region, after which a support vector machine carries out the classification. Tested on the CE-MRI database, this multi-stage system attained an average accuracy of 0.982, a sensitivity of 0.977, a specificity of 0.979, and a Dice score of 0.961. The workflow operates rapidly, completing analysis in 0.42 seconds per scan. These results demonstrate an effective and fast automated alternative to assist clinical tumour evaluation.
Brain tumours represent a leading cause of mortality, making accurate and rapid diagnosis essential for patient outcomes. Manual review of brain scans is demanding and prone to delays. Fast, automated analysis that reliably highlights and categorises tumour tissue can support clinicians in identifying abnormalities earlier, potentially speeding up treatment planning and improving diagnostic consistency in medical settings.
This diagnostic tool is suited for integration into computer-aided clinical decision support software used by radiologists and hospital imaging departments. Operating at an applied and tested research stage on an existing CE-MRI database, the pipeline shows potential for clinical deployment. However, transition to real-world medical use would require further standardisation, testing across broader multi-classifier systems as noted in the research, and clinical validation.
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Cancer associated with the nervous system and brain tumors ranks among the leading causes of death in various countries. Magnetic resonance imaging (MRI) and computed tomography (CT) capture brain images. MRI is pivotal in diagnosing brain tumors and analyzing other brain disorders. Typically, radiologists or experts manually assess MRI images to detect brain tumors and abnormalities in the early stages for appropriate treatment. However, early brain tumor diagnosis is complex, necessitating computerized methods. This research introduces a novel approach for the automated segmentation of brain tumors and a framework for classifying brain tumor regions. The proposed methods comprise several stages: preprocessing, enhancing the coherence of MRI brain images using Contrast Limited Adaptive Histogram Equalization (CLAHE) and diffusion filtering in the first two steps, followed by the segmentation of the region of interest using the Fuzzy C-Means (FCM) clustering technique in the third step. The last step involves classification using the Support Vector Machine (SVM) classifier. The classifier is applied to different brain tumor types, from meningioma to pituitary tumors, utilizing the CE-MRI database. The proposed method exhibits significantly improved contrast and proves the effectiveness of the classification framework, achieving an average sensitivity of 0.977, specificity of 0.979, accuracy of 0.982, and a Dice score (DSC) of 0.961. Furthermore, this method demonstrates a shorter processing time of 0.42 seconds compared to existing approaches. The performance of this method underscores its significance when compared to state-of-the-art methods in terms of sensitivity, specificity, accuracy, and DSC. For future enhancements, it is possible to standardize the approach by incorporating a set of classifiers to increase the robustness of the brain classification method.
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DOI: 10.1109/access.2024.3394541
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