Fractional whale driving training-based optimization enabled transfer learning for detecting autism spectrum disorder

Abstract

Autism Spectrum Disorder (ASD) is a neurological illness that degrades communication and interaction among others. Autism can be detected at any stage. Early detection of ASD is important in preventing the communication, interaction and behavioral outcomes of individuals. Hence, this research introduced the Fractional Whale-driving Driving Training-based Based Optimization with Convolutional Neural Network-based Transfer learning (FWDTBO-CNN_TL) for identifying ASD. Here, the FWDTBO is modelled by the incorporation of Fractional calculus (FC), Whale optimization algorithm (WOA) and Driving Training-based Optimization (DTBO) that trains the hyperparameters of CNN-TL. Moreover, the Convolutional Neural Networks (CNN) utilize the hyperparameters from trained models, like Alex Net and Shuffle Net in such a way that the CNN-TL is designed. To improve the detection efficiency, the nub region was extracted and carried out with the functional connectivity-based Whale Driving Training Optimization (WDTBO) algorithm. Moreover, the TL is tuned by the FWDTBO algorithm. The result reveals that the ASD detection technique, FWDTBO-CNN-TL acquired 90.7 % accuracy, 95.4 % sensitivity, 93.7 % specificity and 93 % f-measure with the ABIDE-II dataset.