Analysis of Brain Imaging Data for the Detection of Early Age Autism Spectrum Disorder Using Transfer Learning Approaches for Internet of Things

In recent years, advanced magnetic resonance imaging (MRI) methods including as functional magnetic resonance imaging (fMRI) and structural magnetic resonance imaging (sMRI) have indicated an increase in the prevalence of neuropsychiatric disorders. Data driven techniques along with medical image analysis techniques, such as computer-assisted diagnosis, can benefit from deep learning. With the use of artificial intelligence (AI) and IoT-based intelligent approaches, it would be convenient to make it easier for autistic children to adopt the new atmospheres. In this study, we have tried to classify and represent learning tasks of the most powerful deep learning network such as Convolution Neural network (CNN) and Transfer Learning algorithm for a combination of data from Autism Brain Imaging Data Exchange (ABIDE I and ABIDE II) datasets. Due to their four-dimensional nature (three spatial dimensions and one temporal dimension), the rs-fMRI data can be used to develop diagnostic biomarkers for brain dysfunction. ABIDE is a global collaboration of scientists, as ABIDE-I and ABIDE-II consists of 1112 rs-fMRI datasets comprising 573 typically developing and 539 autism individuals, 1014 rs-fMRI containing 521 austistic and 593 typical control (TC) respectively, collected from 17 different sites. Our proposed optimized version of CNN achieved 81.56% accuracy. This outperforms prior conventional approaches presented on the ABIDE I datasets.