Improving Classification Performance in Brain Tumor Based on Convolutional Neural Networks
DOI:
https://doi.org/10.26438/ijcse/v13i7.1017Keywords:
CNN, Brain Tumor Classification,, Deep Learning, Grad-CAMAbstract
Accurately identifying brain tumors plays a vital role in early diagnosis and the development of appropriate treatment strategies. Traditional interpretation of MRI scans by radiologists can be time-consuming and subject to variability. This study proposes an automated classification framework based on Convolutional Neural Networks (CNNs) to improve diagnostic consistency and speed. Utilizing a dataset comprising 3,060 MRI images, the model leverages the Grad-CAM technique to visualize key regions influencing its decisions. Rigorous testing was carried out, measuring performance through metrics including accuracy, precision, recall, and specificity. Results demonstrate that the CNN-driven model offers superior classification performance and enhanced transparency when compared to conventional methods. This work contributes to advancing intelligent diagnostic systems and serves as a valuable tool for medical professionals seeking more dependable and rapid evaluations.
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