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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Desmond JE, Glover GH (2002) Estimating sample size in functional MRI (fMRI) neuroimaging studies: statistical power analyses. J Neurosci Methods 118:115–128
Dvornek NC, Ventola P, Pelphrey KA (2017) Identifying autism from resting-state fMRI using long short-term memory networks. In: Machine learning in medical imaging, MLMI (Workshop) (Quebec City, QC), vol 10541, pp 362–370. [PMC free article] [PubMed]
Firat O, Aksan E, Oztekin I (2015) Learning deep temporal representations for fMRI brain decoding. In: 1st International Workshop on Medical Learning Meets Medical Imaging
Han J, Ji X, Hu X (2015) Arousal recognition using audio-visual features and FMRI-based brain response. IEEE Trans Affect Comput 6:337–347. https://doi.org/10.1109/TAFFC.2015.2411280
Hayasaka S, Peiffer AM, Hugenschmidt CE, Laurienti PJ (2007) Power and sample size calculation for neuroimaging studies by non-central random field theory. Neuroimage 37:721–730
Jang H, Plis SM, Calhoun VD (2017) Task-specific feature extraction and classification of fMRI volumes using a deep neural network initialized with a deep belief network: evaluation using sensorimotor tasks. Neuroimage 145:314–328. https://doi.org/10.1016/j.neuroimage.2016.04.003
Joyce K, Hayasaka S (2011) Development of PowerMap: a software package for power analysis in neuroimaging studies. Poster presented at Organization for Human Brain Mapping, Quebec City, Quebec
Mendrik AM, Vincken KL, Kuijf HJ, Breeuwer M, Bouvy WH, De Bresser J, Alansary A, De Bruijne M, Carass A, El-Baz A et al (2015) Mrbrains challenge: online evaluation framework for brain image segmentation in 3T MRI scans. Comput Intell Neurosci 2015:1
Mumford JA (2012) A power calculation guide for fMRI studies. SCAN 7:738–742
Mumford JA, Nichols TE (2008) Power calculation for group fMRI studies accounting for arbitrary design and temporal autocorrelation. Neuroimage 39:261–268
Nie D, Wang L, Gao Y, Shen D (2015) Fully convolutional networks for multimodality isointense infant brain image segmentation. In: IEEE international symposium on biomedical imaging, vol 108, pp 1342–1345
Poldrack RA, Congdon E, Triplett W, Gorgolewski KJ, Karlsgodt KH, Mumford JA, Sabb FW, Freimer NB, London ED, Cannon TD, Bilder RM (2016) A phenome-wide examination of neural and cognitive function. Sci Data 3:160110
Suckling J et al (2014) Are power calculations useful? A multicenter neuroimaging study. Hum Brain Mapp 35:3569–3577 (TECHNICAL REPORT)
Turner BO, Paul EJ, Miller MB, Barbey AK (2018) Small sample sizes reduce the replicability of task-based fMRI studies. Commu Biol 2018:1–62. https://doi.org/10.1038/s42003-018-0073-z
Valverde S, Oliver A, Cabezas M, Roura E, Llado X (2015) Comparison of 10 brain tissue segmentation methods using revisited IBSR annotations. J Magn Reson Imaging 41(1):93–101
Yu Q, Allen EA, Sui J, Arbabshirani MR, Pearlson G, Calhoun DV (2012) Brain connectivity networks in schizophrenia underlying resting-state functional magnetic resonance imaging. Curr Top Med Chem 12(21):2415–2425
Zhao F, Huang Y, Wang L, Xiang T, Tan T (2016) Learning relevance restricted boltzmann machine for unstructured group activity and event understanding. Int J Comput Vis 119:329–345
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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
- Deep learning
- 3D MRI
- 2D Brain segmentation
- Cognitive power of brain