SKIPNet: Spatial Attention Skip Connections for Enhanced Brain Tumor Classification

• URL: http://arxiv.org/abs/2412.07736v1
• Date: Tue, 10 Dec 2024 18:32:42 GMT
• Title: SKIPNet: Spatial Attention Skip Connections for Enhanced Brain Tumor Classification
• Authors: Khush Mendiratta, Shweta Singh, Pratik Chattopadhyay,
• Abstract summary: Early detection of brain tumors is essential for timely treatment, yet access to diagnostic facilities remains limited in remote areas. This study proposes an automated deep learning model for brain tumor detection and classification using MRI data. The model, incorporating spatial attention, achieved 96.90% accuracy, enhancing the aggregation of contextual information for better pattern recognition.
• Score: 3.8233569758620063
• License:
• Abstract: Early detection of brain tumors through magnetic resonance imaging (MRI) is essential for timely treatment, yet access to diagnostic facilities remains limited in remote areas. Gliomas, the most common primary brain tumors, arise from the carcinogenesis of glial cells in the brain and spinal cord, with glioblastoma patients having a median survival time of less than 14 months. MRI serves as a non-invasive and effective method for tumor detection, but manual segmentation of brain MRI scans has traditionally been a labor-intensive task for neuroradiologists. Recent advancements in computer-aided design (CAD), machine learning (ML), and deep learning (DL) offer promising solutions for automating this process. This study proposes an automated deep learning model for brain tumor detection and classification using MRI data. The model, incorporating spatial attention, achieved 96.90% accuracy, enhancing the aggregation of contextual information for better pattern recognition. Experimental results demonstrate that the proposed approach outperforms baseline models, highlighting its robustness and potential for advancing automated MRI-based brain tumor analysis.

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