Abstract
Researchers are continuously making breakthroughs on the impact of deep learning in the medical industry. This approach of Deep Learning (DL) in neuroimaging creates new insights in modification of brain structures during various disorders, helping capture complex relationships that may not have been visible otherwise. The main aim of proposed work is to effectively detect the presence of schizophrenia, a mental disorder that has drastic implications and is hard to spot, using Magnetic Resonance Image Features from fMRI Database. Dataset is then fed to a Neural Network classifier, which learns to predict and give indications for preventing the onset of Schizophrenia using regression models.
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Srivathsan, S., Sreenithi, B., Naren, J. (2020). Detection and Prediction of Schizophrenia Using Magnetic Resonance Images and Deep Learning. In: Mallick, P., Balas, V., Bhoi, A., Chae, GS. (eds) Cognitive Informatics and Soft Computing. Advances in Intelligent Systems and Computing, vol 1040. Springer, Singapore. https://doi.org/10.1007/978-981-15-1451-7_10
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