Bag-of-Features Model for ASD fMRI Classification using SVM

Autism spectrum disorder (ASD) is an abstruse brain disorder in neuroscience research, which lead by challenges of social interactions, speech and nonverbal communication, and repetitive behaviors. Diagnosis of ASD is mostly based on behavioral analysis, which is time-consuming and depends on patient cooperation and examiner expertise. Recently, machine learning(ML) based algorithms are applied for ASD identification. However, there are some limitations using ML approaches such as investigation with functional magnetic resonance imaging (fMRI), depend on region-based analysis, and handling with the big dataset. In this work, we proposed a novel architecture based on the Bag-of-Features model for ASD classification to overcome these challenges. Firstly, we preprocess the images to extract the speeded-up robust features (SURF) from the selected feature point locations. The Bag-of-Feature extraction procedures include feature concatenation, select the most robust feature, and convert to feature vector. After that, we employ K-Means clustering to create a word visual vocabulary from the SURF vector. Then, we encode the Bag-of-Features by adopting coding and quantization techniques to get each class's indexed database. We prefer most tremendous machine learning methods SVM as ASD classifier. Finally, we independently evaluate our proposed architecture's performance using three different datasets from different fields, including ABIDE fMRI preprocessed images and subject's face images. In our experiments, weigh against other state-of-the-art methods that our ML classifiers with Bag-of-Feature extractors reinforce in medication and clinical purposes of ASD.