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@article{156813,
        author = {K.EMILY ESTHER RANI and S. Baulkani},
        title = {Schizophrenia Prediction from rsFMRI Images using RESNET-50 based Classifier},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {5},
        pages = {78-84},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=156813},
        abstract = {Schizophrenia (SZ) is a mental disorder that affects many young people. Early detection and treatment can release the stress of family members and save societal costs. Deep learning in neuro imaging creates new insights in modification of brain structures during various brain disorders. Therefore, resting state functional Magnetic Resonance Image (rsFMRI) data for schizophrenia is used in this paper. At first, the images in the dataset are preprocessed. After that, data augmentation is done and data is splitted into training and testing images. Then, the model based on the deep learning framework RESNET 50 is constructed to extract features and the test images are given to the pre trained model to predict schizophrenia and Healthy Controls (HC). The classification accuracy of95.53% is achieved according to the experimental results. Based on the comparative analysis, we conclude that our model outperforms some recent methods and also increases the schizophrenia prediction accuracy.},
        keywords = {Convolutional Neural Network, Deep learning, Neuro Imaging, Residual Network, Schizophrenia },
        month = {},
        }