Implementation of ANFIS Assisted Modified CNN Classifier for Autism Spectrum Disorder Detection

This Study presents a Medical Image Processing Technique to detect Autism Spectrum Disorder (ASD) using the Patient's facial image. An Algorithm used here is Deep learning, that is used to detect ASD from the input face image of the Patient. The input image is preprocessed and it is moved to data augmentation process to increase its robustness and diversity, then the SIFT (Scale-Invariant Feature Transform) is used for feature extraction which captures distinctive points in the images regardless of scale, rotation, or illumination changes. These features serve as valuable inputs for training the MCNN model, enhancing its ability to discern patterns associated with ASD. The MCNN model is designed and trained to classify the images either ASD or non-ASD category with high accuracy.