Autism spectrum disorder detection using convolutional neural network with transfer learning

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition, which can vary widely among individuals, making it difficult to establish a uniform set of criteria for diagnosis, leads to underdiagnosis or misdiagnosis in previous researches. This research developed the green anaconda one-to-one-based optimiser-based convolutional neural network with transfer learning (GAOOBO_CNN_TL) for ASD classification utilising multimodal data. Firstly, the image pre-processing process is performed by Kuwahara filters and RoI extraction. Then, the input autism data is normalised by using Z-score. Then, the selection of features is conducted by the Hubert index and data augmentation is done by employing bootstrapping. Then, the ASD classification is done by CNN_TL. Then, weight optimisation is executed using GAOOBO, which is the incorporation of green anaconda optimisation (GAO) and one-to-one-based optimiser (OOBO). The GAOOBO_CNN_TL gained accuracy with 92.823%, specificity with 93.127% and sensitivity with 91.997%.