Autism spectrum disorder (ASD) is an intricate neuropsychiatric brain disorder characterized by social deficits and repetitive behaviors. Deep learning approaches have been applied in clinical or behavioral identification of ASD; most erstwhile models are inadequate in their capacity to exploit the data richness. On the other hand, classification techniques often solely rely on region-based summary and/or functional connectivity analysis of functional magnetic resonance imaging (fMRI). Besides, biomedical data modeling to analyze big data related to ASD is still perplexing due to its complexity and heterogeneity. Single volume image consideration has not been previously investigated in classification purposes. By deeming these challenges, in this work, firstly, we design an image generator to generate single volume brain images from the whole-brain image by considering the voxel time point of each subject separately. Then, to classify ASD and typical control participants, we evaluate four deep learning approaches with their corresponding ensemble classifiers comprising one amended Convolutional Neural Network (CNN). Finally, to check out the data variability, we apply the proposed CNN classifier with leave-one-site-out 5-fold cross-validation across the sites and validate our findings by comparing with literature reports. We showcase our approach on large-scale multi-site brain imaging dataset (ABIDE) by considering four preprocessing pipelines, which outperforms the state-of-the-art methods. Hence, it is robust and consistent.