A DEEP LEARNING APPROACH FOR BRAIN TUMOR DETECTION FROM MRI SCANS USING ATTENTION-GUIDED CNNS
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Brain tumors are life threatening conditions of the nervous system, in which prompt diagnosis is important to treat successfully. MRI is one of the methods of diagnosing brain abnormalities but manual interpretation of MRI scan is often time-intensive and subject to human variability. This paper presents a deep learning-based learning method on the basis of a Convolutional Neural Network (CNN) with added attention mechanism. CNN automatically learns spatial patterns and their relevant features based on image data, while the attention mechanism enables the model to focus on the most relevant regions, improving detection performance. The model is trained and evaluated on a publicly available Brain Tumor MRI dataset consisting of 3,264 images categorized into four classes: glioma, meningioma, pituitary tumor, and no tumor. The proposed model provides better classification than conventional methods as it effectively learns discriminative features from MRI images and can help clinicians make more diagnostic and fast decisions.
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Kumar, V. (2026). A Deep Learning Approach for Brain Tumor Detection from MRI Scans Using Attention-Guided CNNS. International Journal of Science, Strategic Management and Technology, 02(05). https://doi.org/10.55041/ijsmt.v2i5.170
Kumar, Vishal. "A Deep Learning Approach for Brain Tumor Detection from MRI Scans Using Attention-Guided CNNS." International Journal of Science, Strategic Management and Technology, vol. 02, no. 05, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i5.170.
Kumar, Vishal. "A Deep Learning Approach for Brain Tumor Detection from MRI Scans Using Attention-Guided CNNS." International Journal of Science, Strategic Management and Technology 02, no. 05 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i5.170.
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