A Transfer Learning-Based Approach for Brain Tumor Classification with Attention Module

Unusual cell growths in the brain or surrounding tissues are termed brain tumors, which can be benign (noncancerous) or malignant (cancerous). Their prevalence, accounting for approximately 2% of all cancer diagnoses according to the World Health Organization (WHO), raises significant public health...

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Bibliographic Details
Published in:2025 International Conference on Electrical, Computer and Communication Engineering (ECCE) pp. 1 - 6
Main Authors: Khan, Md. Ariful Haque, Hossain, Md Shakhawat
Format: Conference Proceeding
Language:English
Published: IEEE 13.02.2025
Subjects:
Accuracy
Artificial Neural Network (ANN)
Biopsy
Brain modeling
Brain tumors
Cancer
Diffusion tensor imaging
Epoch
Image segmentation
Keras
Liquids
Medical diagnostic imaging
MobilenetV2
Self-Attention Mechanism
TensorFlow
Transfer learning
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Summary:Unusual cell growths in the brain or surrounding tissues are termed brain tumors, which can be benign (noncancerous) or malignant (cancerous). Their prevalence, accounting for approximately 2% of all cancer diagnoses according to the World Health Organization (WHO), raises significant public health concerns. Patients often experience symptoms such as headaches, seizures, memory loss, and other neurological issues, impacting their quality of life depending on the tumor's location and size. This study presents a system for classifying brain tumors using a transfer learning model enhanced by a self-attention mechanism. This approach aims to improve feature representation and emphasize relevant regions in input images. We compare the proposed model's performance against existing classification methods, achieving an impressive accuracy of 98.14%.
DOI:10.1109/ECCE64574.2025.11013396