	CT image 2D	63,890 patients with cancer and 171,345 healthy	Discriminate lung cancer lesions in adenocarcinoma, squamous and small cell carcinoma	CNN	Log-Loss error of 0.66 with a sensitivity of 0.87	Oncology	[118]
	CT 2D images	3059 images from several parts of human body	Speed up CT images collection and rebuild the data	Dense Net and a deconvolution model	RMSE of 0.00048	Various	[11]
	CT images 3D	6960 lung nodule regions, 3480 of which were positive samples and rest were negative samples (nonnodule)	CAD to diagnose lung cancer in low-dosage computed tomography	Eye tracking sparse attentional model and convolutional neural network	Accuracy of 0.97	Oncology	[162]
	CT images 2D and text (reports)	9000 training and 1000 testing images	Processing text from CT reports in order to classify their respective images	CNN	Accuracy of 0.95, 0.70 and 0.58 respectively for the three use cases	Various	[12]
	Computed tomography (CT)	Three datasets: 224,316, 112,120 and 15,783	Binary classification of posteroanterior chest xray	CNN	92% accuracy	Radiology	[2] [163]
MRI	Diffusion- weighted imaging maps using MRI	222 patients. 187 treated with rtPA (recombinant tissue-type plasminogen activator)	Decide Acute Ischemic Stroke patients’ treatment through volume lesions prediction	CNN	AUC of 0.88	Neurology- Psychiatry	[2] ; [81]
	Magnetic resonance images	474 patients with schizophrenia and 607 healthy subjects	Schizophrenia detection	Deep discriminant autoencoder network	Accuracy over 0.8	Neurology- Psychiatry	[83]
	Gadoxetic acid-enhanced 2D MRI	144,180 images from 634 patients	Staging liver fibrosis through MR	CNN	AUC values of 0.84, 0.84, and 0.85 for each stage	Gastroenterology	[86]
	Resting state functional magnetic resonance imaging (rs-fMRI), T1 structural cerebral images and phenotypic information	505 individuals with autism and 520 matched typical controls	Identify different autism spectrum disorders	Denoising AE	Accuracy of 0.70	Neurology- Psychiatry	[92]
	3D MRI and PET	93 Alzheimer Disease, 204 MCI Mild Cognitive Impairment converters and normal control subjects	CAD for early Alzheimer disease stages	Multimodal DBM	Accuracy of 0.95, 0.85 and 0.75 for the three use cases	Neurology- Psychiatry	[2] ; [93]