Dyslexia is a disability in language and phonetics, with difficulties in learning and reasoning, affecting around 20% of the worldwide population. Detecting dyslexia at an early stage is vital to provide appropriate remedial teaching aid to improve the learning skills of the affected. The key objective of this study is to identify dyslexia based on Anatomical and Functional MRI data. Convolutional Neural Networks and Time Distributed Convolutional Long-Short Term Memory Neural networks are proposed for screening the neuroimaging data. A multimodal fusion technique is proposed to provide a final combined classification based on the anatomical and functional data. Experimental results demonstrate the performance of the multimodal approach over individual modes of MRI data. The result analysis shows that image segmentation has a significant contribution towards improving classifier performance.
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