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The proposed model for classifying brain tumors, which uses a modified ResNet50V2 architecture with Squeeze-and-Excitation (SE) blocks, performed exceptionally well in classifying MRI images into four groups: pituitary, meningioma, glioma, and no tumor.
With a stunning 99.97% training accuracy and a 99.20% validation accuracy, Cohen Kappa score of 97.85%, an F1 score of 98.3%, a precision of 98.14%, and a recall of 97.78%, the model demonstrated its high efficacy in generalizing to new data.
No tumor received a flawless classification, but glioma and pituitary showed minor errors, according to the confusion matrix analysis.
The model's emphasis on informative traits was strengthened by adding SE blocks, which increased classification accuracy. These findings demonstrate the model's potential for practical use, supporting patients with brain tumors in their early diagnosis and treatment planning.
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IEEE/ICACT20250100 Slide.15
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