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@article{204164,
        author = {Ajay Ramnath Sonawane and Ritesh Wani and Aditya Gite and S. S. Turkane},
        title = {Brain Tumor Detection using Convolutional Neural Network (CNN)},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {1462-1476},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=204164},
        abstract = {This research focuses on accurate brain tumor detection using MRI images through deep learning and transfer learning techniques. The study classifies tumors into four types pituitary, meningioma, glioma, and no tumor using CNN and pre-trained models like MobileNetV2, Vision Transformer (VIT), and VGG16. The results show that all models achieved high accuracy above 95%. Among them, VGG16 performed the best with about 99% training accuracy and 98% testing accuracy, along with excellent precision, recall, and F1-score values. The study concludes that VGG16 is the most effective model for clinical use in supporting radiologists with accurate brain tumor analysis. The final classifications are obtained by merging these features and passing them through a linear classifier. This approach identifies strong and varied characteristics and provides a precise. Proficiency and expertise are required for radiologists to accurately detect brain tumors, a process that requires a significant amount of time. Deep learning technologies are being used and more to automate the diagnosis of brain tumors, resulting in outcomes that are more precise and efficient compared to previous methods.  This research focuses on accurate brain tumor detection using MRI images through deep learning and transfer learning techniques. The study classifies tumors into four types — pituitary, meningioma, glioma, and no tumor using CNN and pre-trained models like MobileNetV2, Vision Transformer (VIT), and VGG16. The results show that all models achieved high accuracy above 95%. Among them, VGG16 performed the best with about 99% training accuracy and 98% testing accuracy, along with excellent precision, recall, and F1-score values. The study concludes that VGG16 is the most effective model for clinical use in supporting radiologists with accurate brain tumor analysis. The final classifications are obtained by merging these features and passing them through a linear classifier. This approach identifies strong and varied characteristics and provides a precise. Proficiency and expertise are required for radiologists to accurately detect brain tumors, a process that requires a significant amount of time. Deep learning technologies are being used and more to automate the diagnosis of brain tumors, resulting in outcomes that are more precise and efficient compared to previous methods.},
        keywords = {A rectified linear unit (ReLU), Convolutional neural networks (CNNs), Deep learning (Dl), Magnetic resonance Imaging (MRI), Visual geometry group (VGG), Vision transformers (ViT)},
        month = {June},
        }