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@article{154207,
        author = {Y.Arpitha and G.Gayathri and K.Lakshmi Tejaswini and A.S.S.V.N.Sai Keerthi and K.Balaji},
        title = {DETECTION AND CLASSIFICATION OF BRAIN TUMOR IN MRI IMAGES USING DEEP CONVOLUTIONAL NETWORK},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {8},
        number = {10},
        pages = {411-416},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=154207},
        abstract = {The detection, segmentation, and extraction from Magnetic Resonance Imaging (MRI) images of contaminated Tumor areas are important concerns; however, a repetitive and extensive task executed by radiologists or clinical experts depend on their expertise. Image processing concepts can visualize the various anatomical structure of the human organ. Recognition of human brain abnormal structures by basic imaging techniques is challenging. To overcome this issue, in this paper, CNN deep learning algorithm was proposed for detecting the Tumor and marking the area of their occurrence with Regional morphological convex hull algorithm. The selected MR image dataset consists of two primary brain Tumors namely malignant and Benign. The proposed algorithm uses CNN architecture for both the region identifier and the classifier network. Here various Feature extraction methods are also extracted. Detection and classification results of the algorithm demonstrate that it is able to achieve a standard deviation 89.77% for meningioma and benign Tumor. As a performance measure, the algorithm achieved a Homogeneity of 92% for all the classes.},
        keywords = {Brain Tumor detection, Malignant, Benign, CNN, Homogeneity, MRI.},
        month = {},
        }