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In this study, a personalized computer aided diagnosis system for autism spectrum disorder is introduced. The proposed system uses resting state functional MRI data to build local classifiers, global classifier, and correlate the classification findings with ADOS behavioral reports. This system is composed of 3 main phases: (i) Data preprocessing to overcome the motion and timing artifacts and normalize the data to standard MNI152 space, (ii) using a small subset (40 subjects) to extract significant activation components, and (iii) utilize the extracted significant components to build a deep learning based diagnosis system for each component, combine the probabilities for global diagnosis and calculate the correlation with ADOS reports. The deep learning based classification system showed accuracies of more than 80% in the significant components, moreover, the global diagnosis accuracy is 93%. Out of the …