Automatic Detection of Autism in Young Children Using Weighted Logarithmic Transformed Data with Optimized Deep Learning. (19th July 2021) - Guruvammal, S; Chellatamilan, T; Deborah, L Jegatha
Abstract: Autism is difficult to recognize in young ones before twenty-four months of age. The symptoms of autism usually occur between twelve to eighteen months. If autism is detected before eighteen months, suitable treatment could be offered to the children, by which normal functioning of the brain could be attained. However, till now, there were no advanced facilities for detecting autism in children on time. That leads to many complex situations while diagnosing the disease. Hence, this paper intends to present an automatic autism detection system, in which the datasets are gathered from the UCI repository. Initially, the weighted transformation of input data is taken, which is carried out to correctly distinguish the interclass labels and reduce the dimension of data. Further, Principal Component Analysis (PCA) helps to perform the role of the dimension reduction process, from which the resultant data is considered as features. Moreover, the dimensionally reduced data as features are classified using the Deep Belief Network (DBN) that recognizes the absence or presence of autism in children. As the main contribution, this paper plans to optimize or tune the decision on the number of hidden neurons in the hidden layer, training algorithm, and activation function of DBN. This optimization process is done in such a way that the error between the predicted and actual output of DBN should be minimum. Accordingly, the optimized DBN is accomplished with the aid of a hybrid algorithm that links both the Crow Search Algorithm (CSA) and the Whale Optimization Algorithm (WOA). The adopted algorithm is known as the Whale Assisted Crow Search Algorithm (W-CSA), because the CSA principle is based on WOA. To the next of implementation, the proposed autism detection model is compared with the conventional approaches, and the results are analysed in an effective manner. Accordingly, from the analysis, it is evident that the accuracy of the proposed algorithm was 4.35%, 2.86%, 1.41%, 1.41%, and 71.43% better than Particle Swarm Optimization (PSO), FireFly (FF), Grey Wolf Optimizer (GWO), WOA, and CS (Crow Search) algorithms, respectively.