RETRACTED ARTICLE: Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda

Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer

Kather, J. N., Pearson, A. T., Halama, N., Jäger, D., Krause, J., Loosen, S. H., Marx, A., Boor, P., Tacke, F., Neumann, U. P., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0462-y · Cited by 1,458

Machine learning and data mining methods in diabetes research

Kavakiotis, I., Tsave, O., Salifoglou, A., Maglaveras, N., Vlahavas, I., & Chouvarda, I. (2017) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2016.12.005 · Cited by 1,445

Real-time cryo-electron microscopy data preprocessing with Warp

Tegunov, D., & Cramer, P. (2019) · Nature Methods · doi:10.1038/s41592-019-0580-y · Cited by 1,292

Artificial Intelligence in Precision Cardiovascular Medicine

Krittanawong, C., Zhang, H., Wang, Z., Aydar, M., & Kitai, T. (2017) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2017.03.571 · Cited by 1,126

Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning

Minaee, S., Kafieh, R., Milan Sonka, A., Yazdani, S., & Soufi, G. J. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101794 · Cited by 1,056

Comparison of Digital Mammography Alone and Digital Mammography Plus Tomosynthesis in a Population-based Screening Program

Skaane, P., Bandos, A. I., Gullien, R., Eben, E. B., Ekseth, U., Haakenaasen, U., Izadi, M., Jebsen, I. N., Jahr, G., Krager, M., et al. (2013) · Radiology · doi:10.1148/radiol.12121373 · Cited by 1,015

Arrhythmia detection using deep convolutional neural network with long duration ECG signals

Yıldırım, Ö., Pławiak, P., Tan, R., & Acharya, U. R. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.09.009 · Cited by 886

Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

Srinivasu, P. N., SivaSai, J. G., Ijaz, M. F., Bhoi, A. K., Kim, W., & Kang, J. J. (2021) · Sensors · doi:10.3390/s21082852 · Cited by 763

Deep Learning in Alzheimer's Disease: Diagnostic Classification and Prognostic Prediction Using Neuroimaging Data

Jo, T., Nho, K., & Saykin, A. J. (2019) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2019.00220 · Cited by 724

Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges

Huang, S., Yang, J., Fong, S. J., & Zhao, Q. (2019) · Cancer Letters · doi:10.1016/j.canlet.2019.12.007 · Cited by 724

Healthfog: an ensemble deep learning based smart healthcare system for automatic diagnosis of heart diseases in integrated iot and fog computing environments

Tuli, S., Basumatary, N., Gill, S. S., Kahani, M., Arya, R. C., Wander, G. S., & Buyya, R. (2020) · Future Generation Computer Systems · doi:10.1016/j.future.2019.10.043 · Cited by 669 · cited 1×

A Hybrid Intelligent System Framework for the Prediction of Heart Disease Using Machine Learning Algorithms

Haq, A. U., Li, J. P., Memon, M. H., Nazir, S., & Sun, R. (2018) · Mobile Information Systems · doi:10.1155/2018/3860146 · Cited by 621

Diabetes Prediction using Machine Learning Algorithms

Mujumdar, A. M., & Vaidehi, V. (2019) · Procedia Computer Science · doi:10.1016/j.procs.2020.01.047 · Cited by 560

A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CT angiography

Oikonomou, E. K., Williams, M. C., Kotanidis, C. P., Desai, M. Y., Marwan, M., Antonopoulos, A. S., Thomas, K. E., Thomas, S., Akoumianakis, I., Fan, L. M., et al. (2019) · European Heart Journal · doi:10.1093/eurheartj/ehz592 · Cited by 534

Structure–Relaxivity Relationships of Magnetic Nanoparticles for Magnetic Resonance Imaging

Zhou, Z., Yang, L., Gao, J., & Chen, X. S. (2019) · Advanced Materials · doi:10.1002/adma.201804567 · Cited by 491

Automated diabetic retinopathy detection in smartphone-based fundus photography using artificial intelligence

Rajalakshmi, R., Subashini, R., Anjana, R. M., & Mohan, V. (2018) · Eye · doi:10.1038/s41433-018-0064-9 · Cited by 462

Applications of Machine Learning Predictive Models in the Chronic Disease Diagnosis

Battineni, G., Sagaro, G. G., Chintalapudi, N., & Amenta, F. (2020) · Journal of Personalized Medicine · doi:10.3390/jpm10020021 · Cited by 418

Real-time artificial intelligence for detection of upper gastrointestinal cancer by endoscopy: a multicentre, case-control, diagnostic study

Luo, H., Xu, G., Li, C., He, L., Luo, L., Wang, Z., Jing, B., Deng, Y., Jin, Y., Yin, L., et al. (2019) · The Lancet Oncology · doi:10.1016/s1470-2045(19)30637-0 · Cited by 417

Artificial Intelligence–Based Breast Cancer Nodal Metastasis Detection: Insights Into the Black Box for Pathologists

Liu, Y., Kohlberger, T., Norouzi, M., Dahl, G. E., Smith, J. L., Mohtashamian, A., Olson, N. H., Peng, L. H., Hipp, J., & Stumpe, M. C. (2018) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2018-0147-oa · Cited by 413

Feature selection and classification systems for chronic disease prediction: A review

Jain, D. K., & Singh, V. P. (2018) · Egyptian Informatics Journal · doi:10.1016/j.eij.2018.03.002 · Cited by 369

Prediction of Type 2 Diabetes using Machine Learning Classification Methods

Tigga, N. P., & Garg, S. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.03.336 · Cited by 363

Data-driven modeling and prediction of blood glucose dynamics: Machine learning applications in type 1 diabetes

Woldaregay, A. Z., Årsand, E., Walderhaug, S., Albers, D., Mamykina, L., Botsis, T., & Hartvigsen, G. (2019) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2019.07.007 · Cited by 358

Predictive modelling and analytics for diabetes using a machine learning approach

Kaur, H., & Kumari, V. (2018) · Applied Computing and Informatics · doi:10.1016/j.aci.2018.12.004 · Cited by 357

An IoT Framework for Heart Disease Prediction Based on MDCNN Classifier

Khan, M. A. (2020) · IEEE Access · doi:10.1109/access.2020.2974687 · Cited by 346

Diabetes detection using deep learning algorithms

Swapna, G., Vinayakumar, R., & Soman, K. P. (2018) · ICT Express · doi:10.1016/j.icte.2018.10.005 · Cited by 345

An augmented reality microscope with real-time artificial intelligence integration for cancer diagnosis

Chen, P.-H. C., Gadepalli, K., MacDonald, R., Liu, Y., Kadowaki, S., Nagpal, K., Kohlberger, T., Dean, J., Corrado, G. S., Hipp, J. D., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0539-7 · Cited by 342

Classification and Visualization of Alzheimer’s Disease using Volumetric Convolutional Neural Network and Transfer Learning

Oh, K., Chung, Y., Kim, K. W., Kim, W., & Oh, I.-S. (2019) · Scientific Reports · doi:10.1038/s41598-019-54548-6 · Cited by 324

Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis

Yue, W., Wang, Z., Chen, H., Payne, A. M., & Liu, X. (2018) · Designs · doi:10.3390/designs2020013 · Cited by 317

Deep learning in ultrasound imaging

van Sloun, R. J. G., Cohen, R., & Eldar, Y. C. (2020) · Proceedings of the IEEE · doi:10.1109/jproc.2019.2932116 · Cited by 316

Expert-Level Diagnosis of Nonpigmented Skin Cancer by Combined Convolutional Neural Networks

Tschandl, P., Rosendahl, C., Akay, B., Argenziano, G., Blum, A., Braun, R., Cabo, H., Gourhant, J., Kreusch, J., Lallas, A., et al. (2019) · JAMA dermatology · doi:10.1001/jamadermatol.2018.4378 · Cited by 313

The Effect of Image Resolution on Deep Learning in Radiography

Sabottke, C. F., & Spieler, B. M. (2020) · Radiology: Artificial Intelligence · doi:10.1148/ryai.2019190015 · Cited by 312

Using Artificial Intelligence to Reduce the Risk of Nonadherence in Patients on Anticoagulation Therapy

Labovitz, D. L., Shafner, L., Reyes Gil, M., Virmani, D., & Hanina, A. (2017) · Stroke · doi:10.1161/strokeaha.116.016281 · Cited by 311

Machine learning and artificial intelligence based Diabetes Mellitus detection and self-management: A systematic review

Chaki, J., Ganesh, S. T., Cidham, S. K., & Theertan, S. A. (2020) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2020.06.013 · Cited by 301

Detection and diagnosis of chronic kidney disease using deep learning-based heterogeneous modified artificial neural network

Ma, F., Sun, T., Liu, L., & Jing, H. (2020) · Future Generation Computer Systems · doi:10.1016/j.future.2020.04.036 · Cited by 290

Data-Driven Cervical Cancer Prediction Model with Outlier Detection and Over-Sampling Methods

Ijaz, M. F., Attique, M., & Son, Y. (2020) · Sensors · doi:10.3390/s20102809 · Cited by 270

A Personalized Healthcare Monitoring System for Diabetic Patients by Utilizing BLE-Based Sensors and Real-Time Data Processing

Alfian, G., Syafrudin, M., Ijaz, M. F., Syaekhoni, M. A., Fitriyani, N. L., & Rhee, J. (2018) · Sensors · doi:10.3390/s18072183 · Cited by 261

Heart Disease Detection by Using Machine Learning Algorithms and a Real-Time Cardiovascular Health Monitoring System

Nashif, S., Raihan, M. R., Islam, M. R., & Imam, H. (2018) · World Journal of Engineering and Technology · doi:10.4236/wjet.2018.64057 · Cited by 255

Management of Hypertension in the Digital Era

Kario, K. (2020) · Hypertension · doi:10.1161/hypertensionaha.120.14742 · Cited by 247

Moderate Carotid Artery Stenosis: MR Imaging–depicted Intraplaque Hemorrhage Predicts Risk of Cerebrovascular Ischemic Events in Asymptomatic Men

Singh, N., Moody, A. R., Gladstone, D. J., Leung, G., Ravikumar, R., Zhan, J. Q., & Maggisano, R. (2009) · Radiology · doi:10.1148/radiol.2522080792 · Cited by 228

Hybrid Prediction Model for Type 2 Diabetes and Hypertension Using DBSCAN-Based Outlier Detection, Synthetic Minority Over Sampling Technique (SMOTE), and Random Forest

Ijaz, M. F., Alfian, G., Syafrudin, M., & Rhee, J. (2018) · Applied Sciences · doi:10.3390/app8081325 · Cited by 224

Applying Machine Learning in Liver Disease and Transplantation: A Comprehensive Review

Spann, A., Yasodhara, A., Kang, J., Watt, K. D. S., Wang, B., Goldenberg, A., & Bhat, M. (2020) · Hepatology · doi:10.1002/hep.31103 · Cited by 209

Artificial Intelligence in Nuclear Medicine

Nensa, F., Demircioğlu, A., & Rischpler, C. (2019) · Journal of Nuclear Medicine · doi:10.2967/jnumed.118.220590 · Cited by 204

Deep Convolution Neural Network Based System for Early Diagnosis of Alzheimer's Disease

Janghel, R. R., & Rathore, Y. K. (2020) · IRBM · doi:10.1016/j.irbm.2020.06.006 · Cited by 204

Deep learning based liver cancer detection using watershed transform and Gaussian mixture model techniques

Das, A., Acharya, U. R., Panda, S. S., & Sabut, S. (2018) · Cognitive Systems Research · doi:10.1016/j.cogsys.2018.12.009 · Cited by 200

Augmented Bladder Tumor Detection Using Deep Learning

Shkolyar, E., Jia, X., Chang, T., Trivedi, D. R., Mach, K. E., Meng, M. Q. ‐., Xing, L., & Liao, J. C. (2019) · European Urology · doi:10.1016/j.eururo.2019.08.032 · Cited by 196

The emerging role of cognitive computing in healthcare: a systematic literature review

Behera, R. K., Bala, P. K., & Dhir, A. (2019) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2019.04.024 · Cited by 193

Current status of artificial intelligence applications in urology and their potential to influence clinical practice

Chen, J., Remulla, D. B., Nguyen, J. H., Dua, A., Liu, Y., Prokar Dasgupta, A., & Hung, A. J. (2019) · British Journal of Urology · doi:10.1111/bju.14852 · Cited by 187

Kidney disease detection and segmentation using artificial neural network and multi-kernel k-means clustering for ultrasound images

Nithya, A., Appathurai, A., Venkatadri, N., Ramji, D. R., & Palagan, C. A. (2019) · Measurement · doi:10.1016/j.measurement.2019.106952 · Cited by 181

A new machine learning method for identifying Alzheimer's disease

Liu, L., Zhao, S., Chen, H., & Wang, A. (2019) · Simulation Modelling Practice and Theory · doi:10.1016/j.simpat.2019.102023 · Cited by 171

Gastrointestinal diseases segmentation and classification based on duo-deep architectures

Khan, M. A., Khan, M. A., Ahmed, F., Mittal, M., Goyal, L. M., Hemanth, D. J., & Satapathy, S. C. (2019) · Pattern Recognition Letters · doi:10.1016/j.patrec.2019.12.024 · Cited by 168

Data-Driven Blood Glucose Pattern Classification and Anomalies Detection: Machine-Learning Applications in Type 1 Diabetes

Woldaregay, A. Z., Årsand, E., Botsis, T., Albers, D. J., Mamykina, L., & Hartvigsen, G. (2019) · Journal of Medical Internet Research · doi:10.2196/11030 · Cited by 164

Deep convolutional neural networks for classification of mild cognitive impaired and Alzheimer's disease patients from scalp EEG recordings

Morabito, F. C., Campolo, M., Ieracitano, C., Ebadi, M. J., Bonanno, L., Bramanti, A., Salvo, S. D., Mammone, N., & Bramanti, P. (2016) · IEEE International Forum on Research and Technology for Society and Industry (RTSI) · doi:10.1109/rtsi.2016.7740576 · Cited by 162

Diabetes Mellitus Affected Patients Classification and Diagnosis through Machine Learning Techniques

Mercaldo, F., Nardone, V., & Santone, A. (2017) · Procedia Computer Science · doi:10.1016/j.procs.2017.08.193 · Cited by 161

Machine learning prediction of incidence of Alzheimer’s disease using large-scale administrative health data

Park, J. H., Cho, H. E., Kim, J. H., Wall, M. M., Stern, Y., Lim, H., Yoo, S., Kim, H. S., & Cha, J. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0256-0 · Cited by 156

Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications

Chui, K. T., Alhalabi, W., Pang, S. S. H., de Pablos, P. O., Liu, R. W., & Zhao, M. (2017) · Sustainability · doi:10.3390/su9122309 · Cited by 153

Automatic detection of tuberculosis bacilli from microscopic sputum smear images using deep learning methods

Panicker, R. O., Kalmady, K. S., Rajan, J., & Sabu, M. K. (2018) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2018.05.007 · Cited by 146

A new approach for classification skin lesion based on transfer learning, deep learning, and IoT system

Rodrigues, D. D. A., Ivo, R. F., Satapathy, S. C., Wang, S., Hemanth, D. J., & Filho, P. P. R. (2020) · Pattern Recognition Letters · doi:10.1016/j.patrec.2020.05.019 · Cited by 146

Prediction of Coronary Heart Disease using Machine Learning

Gonsalves, A. H., Thabtah, F., Mohammad, R. M. A., & Singh, G. (2019) · International Conference on Deep Learning Technologies (ICDLT) · doi:10.1145/3342999.3343015 · Cited by 145

Diagnosis of skin diseases using Convolutional Neural Networks

Rathod, J., Waghmode, V., Sodha, A., & Bhavathankar, P. (2018) · International Conference on Electronics, Communication and Aerospace Technology (ICECA) · doi:10.1109/iceca.2018.8474593 · Cited by 139

Medical Internet of things using machine learning algorithms for lung cancer detection

Pradhan, K. S., & Chawla, P. (2020) · Journal of Management Analytics · doi:10.1080/23270012.2020.1811789 · Cited by 138

Supervised machine learning based gait classification system for early detection and stage classification of Parkinson’s disease

Balaji, E., Brindha, D., & Balakrishnan, R. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106494 · Cited by 136

Improving the Diagnosis of Liver Disease Using Multilayer Perceptron Neural Network and Boosted Decision Trees

Abdar, M., Yen, N. Y., & Hung, J. C.-S. (2017) · Journal of Medical and Biological Engineering · doi:10.1007/s40846-017-0360-z · Cited by 134

Hippocampal atrophy based Alzheimer’s disease diagnosis via machine learning methods

Uysal, G., & Öztürk, M. (2020) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2020.108669 · Cited by 131

Liver Disease Prediction by Using Different Decision Tree Techniques

Nahar, N., & Ara, F. (2018) · International Journal of Data Mining & Knowledge Management Process · doi:10.5121/ijdkp.2018.8201 · Cited by 130

Machine learning and treatment outcome prediction for oral cancer

Chu, C. S., Lee, N. P. Y., Adeoye, J., Thomson, P. J., & Choi, S. W. (2020) · Journal of Oral Pathology and Medicine · doi:10.1111/jop.13089 · Cited by 128

Highly precise risk prediction model for new‐onset hypertension using artificial intelligence techniques

Kanegae, H., Suzuki, K. S., Fukatani, K., Ito, T., Harada, N., & Kario, K. (2019) · Journal of Clinical Hypertension · doi:10.1111/jch.13759 · Cited by 121

Comparing the performance of artificial intelligence and conventional diagnosis criteria for detecting left ventricular hypertrophy using electrocardiography

Kwon, J., Jeon, K., Kim, H. M., Kim, M. J., Lim, S. M., Kim, K., Song, P. S., Park, J., Choi, R. K., & Oh, B. (2019) · EP Europace · doi:10.1093/europace/euz324 · Cited by 119

Iot based patient monitoring and diagnostic prediction tool using ensemble classifier

Ani, R., Krishna, S., Anju, N., Aslam, M. S., & Deepa, O. S. (2017) · International Conference on Advances in Computing, Communications and Informatics (ICACCI) · doi:10.1109/icacci.2017.8126068 · Cited by 117

Comprehensive characterisation of hypertensive heart disease left ventricular phenotypes

Rodrigues, J. C. L., Amadu, A. M., Dastidar, A. G., Szantho, G., Lyen, S. M., Godsave, C., Ratcliffe, L. E. K., Burchell, A. E., Hart, E. C., Hamilton, M. C. K., et al. (2016) · Heart · doi:10.1136/heartjnl-2016-309576 · Cited by 116

Automatic detection of tuberculosis related abnormalities in Chest X-ray images using hierarchical feature extraction scheme

Chandra, T. B., Verma, K., Singh, B. K., Jain, D., & Netam, S. S. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113514 · Cited by 113

Using machine learning on cardiorespiratory fitness data for predicting hypertension: The Henry Ford ExercIse Testing (FIT) Project

Sakr, S., Elshawi, R., Ahmed, A. M., Qureshi, W., Brawner, C. A., Keteyian, S. J., Blaha, M. J., & Al-Mallah, M. H. (2018) · PLoS ONE · doi:10.1371/journal.pone.0195344 · Cited by 112

Towards large-scale case-finding: training and validation of residual networks for detection of chronic obstructive pulmonary disease using low-dose CT

Tang, L. Y. W., Coxson, H. O., Lam, S. C., Leipsic, J. A., Tam, R. C., & Sin, D. D. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30064-9 · Cited by 111

Aiding the Diagnosis of Diabetic and Hypertensive Retinopathy Using Artificial Intelligence-Based Semantic Segmentation

Arsalan, M., Owais, M., Mahmood, T., Cho, S. W., & Park, K. R. (2019) · Journal of Clinical Medicine · doi:10.3390/jcm8091446 · Cited by 108

Deep learning for automated classification of tuberculosis-related chest X-Ray: dataset distribution shift limits diagnostic performance generalizability

Sathitratanacheewin, S., Sunanta, P., & Pongpirul, K. (2020) · Heliyon · doi:10.1016/j.heliyon.2020.e04614 · Cited by 108

Deep and joint learning of longitudinal data for Alzheimer's disease prediction

Lei, B., Yang, M., Yang, P., Zhou, F., Wen, H., Zou, W., Li, X., Wang, T., Xiao, X., & Wang, S. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107247 · Cited by 105

An Improved Multi-Modal based Machine Learning Approach for the Prognosis of Alzheimer’s disease

Khan, A., & Zubair, S. (2020) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2020.04.004 · Cited by 105

A multiple multilayer perceptron neural network with an adaptive learning algorithm for thyroid disease diagnosis in the internet of medical things

Hosseinzadeh, M., Ahmed, O. H., Ghafour, M. Y., Safara, F., Hama, H. K., Ali, S., Vo, B., & Chiang, H. (2020) · The Journal of Supercomputing · doi:10.1007/s11227-020-03404-w · Cited by 105

The Current Research Landscape of the Application of Artificial Intelligence in Managing Cerebrovascular and Heart Diseases: A Bibliometric and Content Analysis

Tran, B. X., Latkin, C. A., Vu, G. T., Nguyen, H. L. T., Nghiem, S. H., Tan, M.-X., Lim, Z. K., Ho, C. S. H., & Ho, R. (2019) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph16152699 · Cited by 100

Artificial Intelligence-Based Classification of Multiple Gastrointestinal Diseases Using Endoscopy Videos for Clinical Diagnosis

Owais, M., Arsalan, M., Choi, J., Mahmood, T., & Park, K. R. (2019) · Journal of Clinical Medicine · doi:10.3390/jcm8070986 · Cited by 99

Prediction of thyroid disease using decision tree ensemble method

Yadav, D. C., & Pal, S. (2020) · Human-Intelligent Systems Integration · doi:10.1007/s42454-020-00006-y · Cited by 99

Future Direction for Using Artificial Intelligence to Predict and Manage Hypertension

Krittanawong, C., Bomback, A. S., Baber, U., Bangalore, S., Messerli, F. H., & Tang, W. H. W. (2018) · Current Hypertension Reports · doi:10.1007/s11906-018-0875-x · Cited by 96

Federated Learning Systems for Healthcare: Perspective and Recent Progress

Kumar, Y., & Singla, R. P. (2021) · Studies in computational intelligence · doi:10.1007/978-3-030-70604-3_6 · Cited by 95

Artificial Intelligence and Hypertension: Recent Advances and Future Outlook

Chaikijurajai, T., Laffin, L. J., & Tang, W. H. W. (2020) · American Journal of Hypertension · doi:10.1093/ajh/hpaa102 · Cited by 94

Retinal specialist versus artificial intelligence detection of retinal fluid from OCT

Keenan, T. D. L., Clemons, T. E., Domalpally, A., Elman, M. J., Havilio, M., Agrón, E., Benyamini, G., & Chew, E. Y. (2021) · Ophthalmology · doi:10.1016/j.ophtha.2020.06.038 · Cited by 93

Classification of Skin Disease using Ensemble Data Mining Techniques

Verma, A. K., Pal, S., & Kumar, S. (2019) · Asian Pacific Journal of Cancer Prevention · doi:10.31557/apjcp.2019.20.6.1887 · Cited by 92

Diagnosis and monitoring of Alzheimer's patients using classical and deep learning techniques

Raza, M., Awais, M., Ellahi, W., Aslam, N., Nguyen, H. X., & Minh, H. L. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.06.038 · Cited by 90

Analysis of tuberculosis severity levels from CT pulmonary images based on enhanced residual deep learning architecture

Gao, X. W., James-Reynolds, C., & Currie, E. (2019) · Neurocomputing · doi:10.1016/j.neucom.2018.12.086 · Cited by 86

Diagnosis of Alzheimer's Disease Using Machine Learning

Lodha, P., Talele, A. K., & Degaonkar, K. (2018) · International Conference on Computing Communication Control and automation · doi:10.1109/iccubea.2018.8697386 · Cited by 85

Diabetic retinopathy detection through novel tetragonal local octa patterns and extreme learning machines

Nazir, T., Irtaza, A., Shabbir, Z., Javed, A., Akram, U., & Mahmood, M. T. (2019) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2019.07.003 · Cited by 79

Advancing Alzheimer’s research: a review of big data promises

Zhang, R., Simon, G., & Yu, F. (2017) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2017.07.002 · Cited by 77

Soft Clustering for Enhancing the Diagnosis of Chronic Diseases over Machine Learning Algorithms

Aldhyani, T. H. H., Alshebami, A. S., & Alzahrani, M. Y. (2020) · Journal of Healthcare Engineering · doi:10.1155/2020/4984967 · Cited by 76

Case-based reasoning support for liver disease diagnosis

Chuang, C. (2011) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2011.06.002 · Cited by 74

Two-stage artificial intelligence model for jointly measurement of atherosclerotic wall thickness and plaque burden in carotid ultrasound: A screening tool for cardiovascular/stroke risk assessment

Biswas, M., Saba, L., Chakrabartty, S., Khanna, N. N., Song, H., Suri, H. S., Sfikakis, P. P., Mavrogeni, S. I., Višković, K., Laird, J. R., et al. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103847 · Cited by 74

Internet of Things, Digital Biomarker, and Artificial Intelligence in Spine: Current and Future Perspectives

Nam, K. H., Kim, D. H., Choi, B. K., & Han, I. H. (2019) · Neurospine · doi:10.14245/ns.1938388.194 · Cited by 70

An intelligent mobile-enabled expert system for tuberculosis disease diagnosis in real time

Shabut, A. M., Tania, M. H., Lwin, K. T., Evans, B. A., Yusof, N. A., Abuhassan, K., & Hossain, M. A. (2018) · Expert Systems with Applications · doi:10.1016/j.eswa.2018.07.014 · Cited by 65

A hybrid intelligent system for skin disease diagnosis

Dabowsa, N. I. A., Amaitik, N., Maatuk, A. M., & Aljawarneh, S. A. (2017) · doi:10.1109/icengtechnol.2017.8308157 · Cited by 62

Utilising artificial intelligence to determine patients at risk of a rare disease: idiopathic pulmonary arterial hypertension

Kiely, D. G., Doyle, O., Drage, E., Jenner, H., Salvatelli, V., Daniels, F. A., Rigg, J., Schmitt, C. J., Samyshkin, Y., Lawrie, A., et al. (2019) · Pulmonary Circulation · doi:10.1177/2045894019890549 · Cited by 62

An Internet of Things (IoT) Application for Predicting the Quantity of Future Heart Attack Patients

Ahmed, F. (2017) · International Journal of Computer Applications · doi:10.5120/ijca2017913773 · Cited by 61

Deep Learning Approach for Generating MRA Images From 3D Quantitative Synthetic MRI Without Additional Scans

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