Enchancing Brain Tumor Disease Classification via SqueezeNet Architecture Integrated with Group Convolution - Gultom, William; Muhathir, Muhathir (JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING, 2025)
Brain tumor classification using MRI images is a major challenge in medical image processing, particularly when facing imbalanced data between classes. This imbalance often leads to model bias toward the majority class and reduces sensitivity to the minority class—patients with tumors. This study aims to analyze the impact of applying Group Convolution techniques to the VGG19 and SqueezeNet architectures to enhance both computational efficiency and classification accuracy. A quantitative experimental approach was employed, implementing Convolutional Neural Networks (CNNs) using the PyTorch framework. The dataset includes two classes, “Yes” (with tumor) and “No” (without tumor), organized into Train, Validation, and Test folders. The models were evaluated by comparing the performance of standard architectures with modified versions integrating Group Convolution. Experimental results show that SqueezeNet with Group Convolution achieved up to 90% accuracy, outperforming the original model. Additionally, the model exhibited significantly improved sensitivity to the minority class, indicating better performance under imbalanced conditions. These findings suggest that Group Convolution enhances not only computational efficiency but also classification capability. Therefore, this technique is applicable in developing automated diagnostic systems. Future research is encouraged to combine Group Convolution with methods such as attention mechanisms to achieve more optimal and reliable classification results.