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Most of them have mainly focused on identifying and classifying the images as either normal or abnormal. Computing brainpower is essential to understand and handle various brain diseases efficiently in critical situations. This paper knuckles down to design and implement a computer-aided framework, enhancing the identification of humans\u2019 cognitive power from their MRI. Images. The proposed framework converts the 3D DICOM images into 2D medical images, preprocessing, enhancement, learning, and extracting various image information to classify it as normal or abnormal and provide the brain\u2019s cognitive power. This study widens the efficient use of machine learning methods, Voxel Residual Network (VRN), with multimodality fusion architecture to learn and analyze the image to classify and predict cognitive power. The experimental results denote that the proposed framework demonstrates better performance than the existing approaches.<\/jats:p>","DOI":"10.3233\/jifs-202069","type":"journal-article","created":{"date-parts":[[2021,6,15]],"date-time":"2021-06-15T13:54:09Z","timestamp":1623765249000},"page":"431-449","source":"Crossref","is-referenced-by-count":0,"title":["Deep learning methods for predicting brain abnormalities and compute human cognitive power using fMRI"],"prefix":"10.1177","volume":"41","author":[{"given":"K.","family":"Palraj","sequence":"first","affiliation":[{"name":"AP, CSE, Srividya College of Engineering &Technology, Virudhunagar, Tamilnadu, India"}]},{"given":"V.","family":"Kalaivani","sequence":"additional","affiliation":[{"name":"CSE, National Engineering College, Kovilpatti, Tamilnadu, India"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-202069_ref1","doi-asserted-by":"crossref","first-page":"3569","DOI":"10.1002\/hbm.22465","article-title":"Are Power Calculations Useful? 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