An automated detection system of autism spectrum disorder using meta-heuristic approach of adaptive LSTM with bayesian learning technique.

Autism spectrum disorder (ASD) is one of the major neurological symptoms affecting young children. Most neurological diseases are captured through speech, voice and changes in sbrain activity. Research leading to ASD diagnosis is done in different ways; still, the early ASD diagnosis is a complex task. Various co-occurring situations may hinder Automated ASD detection, and deep learners effectively tackle such issues and create a better design. Here, a novel automated autism detection approach is proposed employing a deep learning technique with the help of brain image. Initially, the brain images are garnered from the standard dataset links. These gathered images are employed for the pre-processing stage, which is accomplished by using contrast enhancement. Subsequently, the most noteworthy deep features are extracted from the image pre-processed using a multi-atlas-based residual network (MResNet). Finally, the detection process is carried out by influencing the adaptive cascaded attention long short term memory with bayesian learning (ACAL-BL), in which some of the hyperparameters are tuned optimally by the random fixed marine predators algorithm (RFMPA). The performance is examined under Python using various factors and contrasted with other classical models and the results show that our ACAL-BL achieved an FPR of 4.5%, representing relative improvements of 52%, 54%, 56%, 58%, and 60% compared to LSTM, CNN, ANN, auto encoder, and LSTM-Bayesian learning, respectively. Thus, the suggested technique has the tendency to exploit the outstanding results that aid clinical practitioners to diagnose the disease earlier.