A Highly Accurate Computational Approach for Brain Tumor Classification Task Based on Improved Pre-trained InceptionV3 Model

The incidence of brain tumors is rising, and current diagnostic methods, including biopsy and imaging screenings, have limitations. The lengthy and risky nature of biopsies, and the potential for subjective errors in image interpretation by radiologists, highlight the need for improvement. risky nature of biopsies, and the potential for subjective errors in image interpretation by radiologists, highlight the need for improvement. Advances in technology, particularly artificial intelligence, offer a new direction. This study combines the InceptionV3 model with ResNet50 to This study combines the InceptionV3 model with ResNet50 to create an improved brain tumor identification system. Preprocessing MRI datasets and training models indicate that this fusion enhances accuracy and efficiency. Various combination methods are explored, confirming the benefits of integrating ResNet50's residual structure into InceptionV3. The improved model's validity is confirmed. improved model's validity is confirmed across different datasets. This AI technology holds significant potential for medical image diagnosis, with This AI technology holds significant potential for medical image diagnosis, with future research focusing on improving accuracy and generalization through deep learning and transfer learning.