Abstract:
Brain tumor detection and classification using deep learning techniques have shown great potential in
assisting with early diagnosis, providing clinical guidance, and improving treatment effectiveness in
neurology. However, existing studies have overlooked important factors and suffered from increased
computation time. In this research, we address these limitations by leveraging a broader range of brain
Medical Record images (MRI) with varying weights and contrast enhancement methods. Our chosen
model, SqueezeNet, tackles these challenges effectively.
Using a dataset comprising various modalities of MR images obtained from the University of Gondar
Specialized Hospital, including both normal brain scans and scans depicting different tumor conditions,
we employ the SqueezeNet model for accurate classification. Preprocessing techniques, such as
converting DICOM to JPG and applying filtering and segmentation methods, enhance performance.
Our proposed model achieves a SoftMax classification accuracy of 97.98% while maintaining
computational efficiency with a computation time of 13,868.13 seconds. Compared to other evaluated
models, SqueezeNet exhibits the shortest computation time, surpassing the accuracy of models like
VGGNet19 and AlexNet. Furthermore, we explore concatenation models, and the combination of
SqueezeNet with VGG19 demonstrates the highest accuracy of 97.36%.
In summary, our research contributes to the advancement of brain tumor detection and classification by
addressing previous limitations and achieving high accuracy and computational efficiency. Future
studies should focus on integrating tumor grading, exploring a broad
