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@article{182263,
        author = {shalini yadav and Dr. satendra kuamr},
        title = {brain tumor detection in medical image using deep learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {2},
        pages = {1358-1363},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=182263},
        abstract = {Brain Tumor passes significant health risks due to their high mortality rates and challenges in easy diagnosis. Advances in medical imaging, particularly MRI, combined with artificial intelligence (AI) have revolutionized tumor detection, segmentation and classification. This study presents a comparative convolutional neural network (CNN) modulus of brain tumor detection on MRI medical images. Brain tumors are among the deadliest forms of cancer, requiring accurate and timely diagnosis to improve patient survival rates. Magnetic Resonance Imaging (MRI) plays a pivotal role in detecting brain abnormalities. In recent years, deep learning (DL) techniques, particularly Convolutional Neural Networks (CNNs), have significantly enhanced the automatic detection and classification of brain tumors in medical images. This paper presents a comprehensive approach to brain tumor detection using deep learning, evaluating model performance on publicly available datasets. The study also discusses the challenges and future prospects in applying deep learning for real-world clinical use.},
        keywords = {Brain Tumor Detection, Deep Learning, Medical Imaging, MRI, Convolutional Neural Networks, Segmentation, Classification.},
        month = {July},
        }