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@article{185757,
        author = {Dr.Bere Sachin S. and Ms.Vaibhavi Dhanaji Ghorpade and Ms.Sanika Jitendra Patekar and Ms.Sanika Dagadu Satre and Ms.Gauri Rajesh Wakchaure and Prof.Akshay M.Suryawanshi},
        title = {Brain Tumor Detection and Classification Using Deep Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {5},
        pages = {2848-2851},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=185757},
        abstract = {This paper introduces a deep learning-based approach for detecting brain tumors through transfer learning and MRI images. To categorize MRI scans into four groups—glioma, meningioma, pituitary tumor, and no tumor—a pre-trained VGG16 model is refined. To enhance model generalization, images are preprocessed using techniques like scaling, normalization, and augmentation. With 97.45% accuracy and over 96% precision, recall, and F1-score, the system performs well. The usefulness of VGG16 for medical image categorization is demonstrated by the model’s considerable improvement over a baseline CNN. The findings point to a great deal of promise for clinical integration, providing radiologists with a quick and trustworthy way to get a second opinion. This method reduces human error, improves diagnostic efficiency, and demonstrates the usefulness of transfer learning in healthcare applications.},
        keywords = {Brain Tumor Detection, Magnetic Resonance Imaging (MRI), Convolutional Neural Network (CNN), Deep Learning, Transfer Learning, VGG16, Medical Image Analysis, Image Preprocessing, Data Augmentation, Automated Diagnosis.},
        month = {October},
        }