Abstract
In modern times, digital medical images play a significant progression in clinical diagnosis to treat the populace earlier to hoard their lives. Magnetic resonance imaging (MRI) is one of the most advanced medical imaging modalities that facilitate scanning various parts of the human body like the head, chest, abdomen, and pelvis and identify the diseases. Numerous studies on the same discipline have proposed different algorithms, techniques, and methods for analyzing medical digital images, especially MRI. 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' cognitive power from their MRI Images. The proposed framework converts the 3D DICOM images into 2D medical images, pre-processing, enhancement, learning, and extracting various image information to classify it as normal or abnormal and provide the brain's 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.
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Palraj, K., Kalaivani, V. Predicting the abnormality of brain and compute the cognitive power of human using deep learning techniques using functional magnetic resonance images. Soft Comput 25, 14461–14478 (2021). https://doi.org/10.1007/s00500-021-06292-1
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DOI: https://doi.org/10.1007/s00500-021-06292-1