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@article{170355,
        author = {P Roopa Ranjani},
        title = {BRAIN TUMOR SEGMENTATION AND CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORKS},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {7},
        pages = {341-347},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=170355},
        abstract = {Automatic defects detection in MRI images is very important in many diagnostic and therapeutic applications. Magnetic resonance imaging (MRI) is a widely used imaging technique to assess these tumors, but the large amount of data produced by MRI prevents manual segmentation in a reasonable time, limiting the use of precise quantitative measurements in the clinical practice. So, automatic and reliable segmentation methods are required. Automatic brain tumor detection method increases the accuracy and decreases the diagnosis time. The proposed system is to classify the tissues into three classes of normal, benign and malignant. The diagnosis method consists of pre-processing of MR images, segmentation, feature extraction, and classification. Image segmentation is used to cluster the pixels which might be used to classify the disease or to detect a tumor. Neural Network (NN) are employed to classify into normal and abnormal brain. Convolutional Neural Network (CNN) is used as a classifier to compare the given image and the image in the database. If the tumor is identified while comparing each pixel, it display the message box the tumor is affected, after completing the NN training. Overall, this proposes a novel method of brain MRI image segmentation using the conventional neural network to increase the accuracy compared to the conventional methods.},
        keywords = {Neural Network (NN), Convolutional Neural Network (CNN), Magnetic resonance imaging (MRI), Space Invariant Artificial Neural Networks(SIANN), NYU Object Recognition Benchmark(NORB), Synthetic Aperture Radar(SAR), Optical Coherence Tomography(OCT).},
        month = {December},
        }