Artificial neural networks in medical diagnosis

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Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance

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Artificial Neural Networks

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Artificial Intelligence in Medical Diagnosis

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Feature selection with neural networks

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Development of a decision support system for diagnosis and grading of brain tumours using in vivo magnetic resonance single voxel spectra

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Artificial neural networks for diagnosis and survival prediction in colon cancer

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Segmentation of multiple sclerosis lesions in MR images: a review

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Application of Artificial Intelligence for COVID-19 Epidemic: An Exploratory Study, Opportunities, Challenges, and Future Prospects

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Breath analysis by ultra-sensitive broadband laser spectroscopy detects SARS-CoV-2 infection

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Discrete-time high order neural network identifier trained with cubature Kalman filter

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MLP-LOA: a metaheuristic approach to design an optimal multilayer perceptron

Bansal, P., Gupta, S., Kumar, S., Sharma, S., & Sharma, S. (2019) · Soft Computing · doi:10.1007/s00500-019-03773-2 · Cited by 31

Risk factor identification and prediction models for prolonged length of stay in hospital after acute ischemic stroke using artificial neural networks

Yang, C., Bamodu, O. A., Chan, L., Chen, J., Hong, C., Huang, Y.-T., & Chung, C. (2023) · Frontiers in Neurology · doi:10.3389/fneur.2023.1085178 · Cited by 33

Artificial Intelligence in Resuscitation: A Scoping Review

Viderman, D., Abdildin, Y. G., Batkuldinova, K., Badenes, R., & Bilotta, F. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12062254 · Cited by 32

AI APPLICATIONS IN EMERGING TECH SECTORS: A REVIEW OF AI USE CASES ACROSS HEALTHCARE, RETAIL, AND CYBERSECURITY

Babar, Z., Paul, R., Barua, T., & Rahman, M. A. (2025) · doi:10.63125/245ec865 · Cited by 31

The Relationship Between Bruch's Membrane Opening-Minimum Rim Width and Retinal Nerve Fiber Layer Thickness and a New Index Using a Neural Network

Park, K., Kim, J., & Lee, J. W. (2018) · Translational Vision Science & Technology · doi:10.1167/tvst.7.4.14 · Cited by 30

Prediction of Heart Diseases Using Data Mining Techniques

Masih, N., & Ahuja, S. (2018) · International Journal of Big Data and Analytics in Healthcare · doi:10.4018/ijbdah.2018070101 · Cited by 30

Augmenting Surgery: Medical Students’ Assessment and Ergonomics of 3D Holograms vs. CT Scans for Pre-Operative Planning

Triberti, S., Petrella, F., Gorini, A., Pappalardo, O. A., Sebri, V., Savioni, L., Redaelli, A. C. L., & Pravettoni, G. (2021) · EAI Endorsed Transactions on Pervasive Health and Technology · doi:10.4108/eai.8-1-2021.167844 · Cited by 29

Coronary Heart Disease Interpretation Based on Deep Neural Network

Darmawahyuni, A., Nurmaini, S., & Firdaus, F. (2019) · Computer Engineering and Applications Journal · doi:10.18495/comengapp.v8i1.288 · Cited by 29

Data-Driven Modeling Approach to Predict the Recovery Performance of Low-Salinity Waterfloods

Kalam, S., Khan, R. A., Khan, S., Faizan, M., Amin, M. N., Ajaib, R., & Abu-Khamsin, S. A. (2021) · Natural Resources Research · doi:10.1007/s11053-020-09803-3 · Cited by 29

The development trend of artificial intelligence in medical: A patentometric analysis

Xin, Y., Wang, M., & Zhou, Y. X. (2021) · Artificial Intelligence in the Life Sciences · doi:10.1016/j.ailsci.2021.100006 · Cited by 29

Differentiating smokers and nonsmokers based on Raman spectroscopy of oral fluid and advanced statistics for forensic applications

Al‐Hetlani, E., Halámková, L., Amin, M. O., & Lednev, I. K. (2019) · Journal of Biophotonics · doi:10.1002/jbio.201960123 · Cited by 29

Artificial Neural Network Parameter Tuning Framework For Heart Disease Classification

Yazid, M. H. A., Satria, M. H., Talib, S., & Azman, N. (2018) · Proceeding of the Electrical Engineering Computer Science and Informatics · doi:10.1109/eecsi.2018.8752821 · Cited by 28

Is There a Place for Responsible Artificial Intelligence in Pandemics? A Tale of Two Countries

El-Haddadeh, R., Fadlalla, A. M., & Hindi, N. M. (2021) · Information Systems Frontiers · doi:10.1007/s10796-021-10140-w · Cited by 28

Mass excess estimations using artificial neural networks

Özdoğan, H., Üncü, Y. A., Şekerci, M., & Kaplan, A. (2022) · Applied Radiation and Isotopes · doi:10.1016/j.apradiso.2022.110162 · Cited by 28

A Logistic Regression and Artificial Neural Network-Based Approach for Chronic Disease Prediction: A Case Study of Hypertension

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Principles of Machine Learning and Its Application to Thermal Barrier Coatings

Liu, Y., Chen, K., Kumar, A., & Patnaik, P. (2023) · Coatings · doi:10.3390/coatings13071140 · Cited by 27

Investigation into the Application of Image Modeling Technology in the Field of Computer Graphics

Doungmala, P. (2023) · International Journal for Applied Information Management · doi:10.47738/ijaim.v3i2.53 · Cited by 27

Heart Diseases Diagnoses using Artificial Neural Network

Ajam, N. (2015) · Journals & Books Hosting (International Knowledge Sharing Platform) · Cited by 27

Boosting backpropagation algorithm by stimulus-sampling: Application in computer-aided medical diagnosis

Gorunescu, F., & Belciug, S. (2016) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2016.08.004 · Cited by 27

Predicting major neurologic improvement and long-term outcome after thrombolysis using artificial neural networks

Chung, C., Hong, C., Huang, Y.-H., Su, E. C., Chan, L., Hu, C., & Chiu, H. (2020) · Journal of the Neurological Sciences · doi:10.1016/j.jns.2020.116667 · Cited by 27

Tooth and Bone Parameters in the Assessment of the Chronological Age of Children and Adolescents Using Neural Modelling Methods

Zaborowicz, K., Biedziak, B., Olszewska, A., & Zaborowicz, M. (2021) · Sensors · doi:10.3390/s21186008 · Cited by 27

Development and validation of prediction models for hypertension risks: A cross-sectional study based on 4,287,407 participants

Ji, W., Zhang, Y., Cheng, Y., Wang, Y., & Zhou, Y. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.928948 · Cited by 27

Machine learning-based model for predicting the esophagogastric variceal bleeding risk in liver cirrhosis patients

Hou, Y., Yu, H., Zhang, Q., Yang, Y., Liu, X., Wang, X., & Jiang, Y. (2023) · Diagnostic Pathology · doi:10.1186/s13000-023-01293-0 · Cited by 27

Controlled release of doxorubicin from gelatin-based nanoparticles: theoretical and experimental approach

Fatima, W., Batool, S. R., Mushtaq, F., Aslam, M., Raza, Z. A., & Nazeer, M. A. (2024) · Materials Advances · doi:10.1039/d3ma00825h · Cited by 27

Remotely Sensed Soil Data Analysis Using Artificial Neural Networks: A Case Study of El-Fayoum Depression, Egypt

Amato, F., Havel, J., Gad, A., & El-Zeiny, A. (2015) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi4020677 · Cited by 27

Artificial intelligence, robotics, ethics, and the military: a Canadian perspective

Wasilow, S., & Thorpe, J. B. (2019) · AI Magazine · doi:10.1609/aimag.v40i1.2848 · Cited by 42

Identifying Tax Evasion in Mexico with Tools from Network Science and Machine Learning

Zumaya, M., Guerrero, R., Islas, E., Pineda, O., Gershenson, C., Íñiguez, G., & Pineda, C. (2021) · Understanding complex systems · doi:10.1007/978-3-030-81484-7_6 · Cited by 29

Artificial Neural Network Modelling for Optimizing the Optical Parameters of Plasmonic Paired Nanostructures

Verma, S., Chugh, S., Ghosh, S., & Rahman, B. M. A. (2022) · Nanomaterials · doi:10.3390/nano12010170 · Cited by 27

Process-Monitoring-for-Quality — Big Models

Escobar, C. A., Abell, J. A., Hernández-de-Menéndez, M., & Morales-Menéndez, R. (2018) · Procedia Manufacturing · doi:10.1016/j.promfg.2018.07.153 · Cited by 26

Toward Predicting 30-Day Readmission Among Oncology Patients: Identifying Timely and Actionable Risk Factors

Hwang, S., Urbanowicz, R., Lynch, S., Vernon, T., Bresz, K., Giraldo, C., Kennedy, E., Leabhart, M., Bleacher, T., Ripchinski, M. R., et al. (2023) · JCO Clinical Cancer Informatics · doi:10.1200/cci.22.00097 · Cited by 25

Neural lab a simulator for artificial neural networks

Ledesma, S., Ibarra-Manzano, M.-A., García-Hernández, M. G., & Almanza-Ojeda, D.-L. (2017) · 2017 Computing Conference · doi:10.1109/sai.2017.8252175 · Cited by 25

A novel diagnostic and prognostic approach for unresponsive patients with anthroponotic cutaneous leishmaniasis using artificial neural networks

Bamorovat, M., Sharifi, I., Rashedi, E., Shafiian, A., Sharifi, F., Khosravi, A., & Tahmouresi, A. (2021) · PLoS ONE · doi:10.1371/journal.pone.0250904 · Cited by 27

Transfer physics informed neural network: a new framework for distributed physics informed neural networks via parameter sharing

Manikkan, S., & Srinivasan, B. (2022) · Engineering With Computers · doi:10.1007/s00366-022-01703-9 · Cited by 25

Develop a diagnostic tool for dementia using machine learning and non-imaging features

Wang, H., Sheng, L. Q., Xu, S., Jin, Y., Jin, X., Qiao, S., Chen, Q., Xing, W., Zhao, Z., Yan, J., et al. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.945274 · Cited by 27

Artificial neural network modeling of scale-dependent dynamic capillary pressure effects in two-phase flow in porous media

Abidoye, L. K., & Das, D. B. (2014) · Journal of Hydroinformatics · doi:10.2166/hydro.2014.079 · Cited by 25

Recurrent Neural Network Architectures for Analysing Biomedical Data Sets

Khalaf, M., Hussain, A. J., Keight, R., Al‐Jumeily, D., Keenan, R., Chalmers, C., Fergus, P., Salih, W. M., Abd, D. H., & Idowu, I. O. (2017) · International Conference on Developments in eSystems Engineering (DeSE) · doi:10.1109/dese.2017.12 · Cited by 25

Epistemic virtues and data-driven dreams: On sameness and difference in the epistemic cultures of data science and psychiatry

Stevens, M., Wehrens, R., & Bont, A. A. D. (2020) · Social Science & Medicine · doi:10.1016/j.socscimed.2020.113116 · Cited by 25

Machine learning at the edge for AI-enabled multiplexed pathogen detection

Ganjalizadeh, V., Meena, G. G., Stott, M. A., Hawkins, A. R., & Schmidt, H. (2023) · Scientific Reports · doi:10.1038/s41598-023-31694-6 · Cited by 24

Toward artificial intelligence (AI) applications in the determination of COVID-19 infection severity: considering AI as a disease control strategy in future pandemics

Ghaderzadeh, M., Asadi, F., Ghorbani, N. R., Almasi, S., & Taami, T. (2023) · Iranian Journal of Blood and Cancer · doi:10.61186/ijbc.15.3.93 · Cited by 24

Hybrid Artificial Neural Network by Using Differential Search Algorithm for Solving Power Flow Problem

Abacı, K., & Yamaçlı, V. (2019) · Advances in Electrical and Computer Engineering · doi:10.4316/aece.2019.04007 · Cited by 24

Decision making on vestibular schwannoma treatment: predictions based on machine-learning analysis

Profant, O., Bureš, Z., Balogová, Z., Betka, J., Fík, Z., Chovanec, M., & Voráček, J. (2021) · Scientific Reports · doi:10.1038/s41598-021-97819-x · Cited by 24

A new efficient algorithm based on feedforward neural network for solving differential equations of fractional order

Admon, M. R., Senu, N., Ahmadian, A., Majid, Z. A., & Salahshour, S. (2022) · Communications in Nonlinear Science and Numerical Simulation · doi:10.1016/j.cnsns.2022.106968 · Cited by 24

Cardiovascular Disease Prediction Using Risk Factors: A Comparative Performance Analysis of Machine Learning Models

Hussain, A., & Aslam, A. (2024) · Journal on artificial intelligence · doi:10.32604/jai.2024.050277 · Cited by 24

Quantum associative memory with linear and non-linear algorithms for the diagnosis of some tropical diseases

Njafa, J.-P. T., & Engo, S. G. N. (2017) · Neural Networks · doi:10.1016/j.neunet.2017.09.002 · Cited by 24

Predicting Movie Box Office Profitability: A Neural Network Approach

Ginmu, R. T., & Zulkernine, F. H. (2016) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2016.0117 · Cited by 24

Detecting central fixation by means of artificial neural networks in a pediatric vision screener using retinal birefringence scanning

Gramatikov, B. I. (2017) · BioMedical Engineering OnLine · doi:10.1186/s12938-017-0339-6 · Cited by 24

Survey of Image Processing Techniques for Brain Pathology Diagnosis: Challenges and Opportunities

Cenek, M., Hu, M., York, G. E., & Dahl, S. K. (2018) · Frontiers in Robotics and AI · doi:10.3389/frobt.2018.00120 · Cited by 24

Comparative evaluation of the predictability of neural network methods on the flammability characteristics of extruded polystyrene from microscale combustion calorimetry

Mensah, R. A., Jiang, L., Asante‐Okyere, S., Xu, Q., & Jin, C. (2019) · Journal of Thermal Analysis and Calorimetry · doi:10.1007/s10973-019-08335-0 · Cited by 24

Improved ICU mortality prediction based on SOFA scores and gastrointestinal parameters

Aperstein, Y., Cohen, L., Bendavid, I., Cohen, J. D., Grozovski, E., Rotem, T., & Singer, P. (2019) · PLoS ONE · doi:10.1371/journal.pone.0222599 · Cited by 24

Application of Neural Networks and Machine Learning in Image Recognition

Galić, D., Stojanović, Z., & Čajić, E. (2024) · Tehnicki vjesnik - Technical Gazette · doi:10.17559/tv-20230621000751 · Cited by 23

Identifying prognostic factors and developing accurate outcome predictions for in-hospital cardiac arrest by using artificial neural networks

Chung, C., Chiu, W.-T., Huang, Y.-H., Chan, L., Hong, C., & Chiu, H. (2021) · Journal of the Neurological Sciences · doi:10.1016/j.jns.2021.117445 · Cited by 23

Development of a deep learning model for automatic localization of radiographic markers of proposed dental implant site locations

Alsomali, M., Alghamdi, S., Alotaibi, S., Alfadda, S. A., Altwaijry, Ν., Al-Turaiki, I. M., & Al-Ekrish, A. A. (2022) · The Saudi Dental Journal · doi:10.1016/j.sdentj.2022.01.002 · Cited by 23

ImplantFormer: vision transformer-based implant position regression using dental CBCT data

Yang, X., Li, X., Li, X., Wu, P., Shen, L., & Deng, Y. (2024) · Neural Computing and Applications · doi:10.1007/s00521-023-09411-1 · Cited by 23

Prediction model development for petroleum refinery wastewater treatment

Hayder, G., Ramli, M. Z., Malek, M. A., Khamis, A., & Hilmin, N. M. (2014) · Journal of Water Process Engineering · doi:10.1016/j.jwpe.2014.08.006 · Cited by 23

Centered Kernel Alignment Enhancing Neural Network Pretraining for MRI-Based Dementia Diagnosis

Peña, D. A. C., Collazos-Huertas, D. F., & Castellanos-Dominguez, G. (2016) · Computational and Mathematical Methods in Medicine · doi:10.1155/2016/9523849 · Cited by 23

NEURAL NETWORK–BASED RISK PREDICTION AND SIMULATION FRAMEWORK FOR MEDICAL IOT CYBERSECURITY: AN ENGINEERING MANAGEMENT MODEL FOR SMART HOSPITALS

Islam, M. T., Ahmad, S. U., Rahman, M. A., & Rahaman, M. A. (2024) · doi:10.63125/g0mvct35 · Cited by 22

An Automated System for Skeletal Maturity Assessment by Extreme Learning Machines

Mansourvar, M., Shamshirband, S., Raj, R. G., Gunalan, R., & Mazinani, I. (2015) · PLoS ONE · doi:10.1371/journal.pone.0138493 · Cited by 22

Factors of hospitalization expenditure of the genitourinary system diseases in the aged based on “System of Health Account 2011” and neural network model

He, J., Yin, Z., Duan, W., Wang, Y., & Wang, X. (2018) · Journal of Global Health · doi:10.7189/jogh.08.020504 · Cited by 22

A Multi-Agent Approach for Personalized Hypertension Risk Prediction

Abrar, S., Loo, C. K., & Kubota, N. (2021) · IEEE Access · doi:10.1109/access.2021.3074791 · Cited by 25

The Use of Artificial Intelligence and Machine Learning in Surgery: A Comprehensive Literature Review

Dagli, M. M., Rajesh, A., Asaad, M., & Butler, C. E. (2021) · The American Surgeon · doi:10.1177/00031348211065101 · Cited by 22

GGA‐MLP: A Greedy Genetic Algorithm to Optimize Weights and Biases in Multilayer Perceptron

Bansal, P., Lamba, R., Jain, V., Jain, T., Shokeen, S., Kumar, S., Singh, P. K., & Khan, B. (2022) · Contrast Media & Molecular Imaging · doi:10.1155/2022/4036035 · Cited by 22

Prediction of Parkinson’s disease based on artificial neural networks using speech datasets

Liu, W., Liu, J., Peng, T., Wang, G., Bălaş, V. E., Geman, O., & Chiu, H. (2022) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-022-03825-w · Cited by 22

Predicting mortality in brain stroke patients using neural networks: outcomes analysis in a longitudinal study

Someeh, N., Mirfeizi, M., Jafarabadi, M. A., Alinia, S., Farzipoor, F., & Shamshirgaran, S. M. (2023) · Scientific Reports · doi:10.1038/s41598-023-45877-8 · Cited by 22

Predicting students’ dropout at university using Artificial Neural Networks

Siri, A. (2015) · CINECA IRIS Institutial Research Information System (University of Genoa) · Cited by 22

Automatic Screening of Diabetic Retinopathy Images with Convolution Neural Network Based on Caffe Framework

Jiang, Y., Wu, H., & Dong, J. (2017) · International Conference on Medical and Health Informatics (ICMHI) · doi:10.1145/3107514.3107523 · Cited by 22

QuantBayes: Weight Optimization for Memristive Neural Networks via Quantization-Aware Bayesian Inference

Zhou, Y., Hu, X., Wang, L., Zhou, G., & Duan, S. (2021) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2021.3115787 · Cited by 22

Discovery of a novel tetrapeptide as glucose homeostasis modulator with bifunctionalities of targeting DPP‐IV and microbiota

Chen, H., Li, W., Hu, W., Liu, J., Zhang, C., Wang, Y., Zhang, C., Zhang, X., Chen, S., Nie, Q., et al. (2025) · iMeta · doi:10.1002/imt2.70072 · Cited by 21

Artificial Intelligence in Computer Vision: Cardiac MRI and Multimodality Imaging Segmentation

Kwan, A. C., Salto, G., Cheng, S. C., & Ouyang, D. (2021) · Current Cardiovascular Risk Reports · doi:10.1007/s12170-021-00678-4 · Cited by 21

Black-Box Audio Adversarial Attack Using Particle Swarm Optimization

Mun, H., Seo, S., van Son, B. L., & Yun, J. (2022) · IEEE Access · doi:10.1109/access.2022.3152526 · Cited by 22

An Automatic Diagnosis System for Hepatitis Diseases Based on Genetic Wavelet Kernel Extreme Learning Machine

Avcı, D. (2016) · Journal of Electrical Engineering and Technology · doi:10.5370/jeet.2016.11.4.993 · Cited by 21

Computerized prediction system for the route of delivery (vaginal birth versus cesarean section)

Beksaç, M. S., Tanaçan, A., Bacak, H. O., & Leblebi̇ci̇oğlu, K. (2018) · Journal of Perinatal Medicine · doi:10.1515/jpm-2018-0022 · Cited by 21

STATCOM Estimation Using Back-Propagation, PSO, Shuffled Frog Leap Algorithm, and Genetic Algorithm Based Neural Networks

Soodi, H. A., & Vural, A. M. (2018) · Computational Intelligence and Neuroscience · doi:10.1155/2018/6381610 · Cited by 21

A Neural Network based Digital Forensics Classification

Mohammad, R. M. A. (2018) · IEEE/ACS International Conference on Computer Systems and Applications (AICCSA) · doi:10.1109/aiccsa.2018.8612868 · Cited by 21

Artificial intelligence methods for a Bayesian epistemology‐powered evidence evaluation

Pretis, F. D., Landes, J., & Peden, W. (2021) · Journal of Evaluation in Clinical Practice · doi:10.1111/jep.13542 · Cited by 21

Review of Medical Disease Symptoms Prediction Using Data Mining Technique

Sah, R. D., & Sheetalani, J. (2017) · IOSR Journal of Computer Engineering · doi:10.9790/0661-1903015970 · Cited by 21

Diagnosis of common headaches using hybrid expert-based systems

Khayamnia, M., Yazdchi, M., Heydari, A., & Foroughipour, M. (2019) · Journal of Medical Signals & Sensors · doi:10.4103/jmss.jmss_47_18 · Cited by 20

ARGAN: Adversarially Robust Generative Adversarial Networks for Deep Neural Networks Against Adversarial Examples

Choi, S.-H., Shin, J., Liu, P., & Choi, Y.-H. (2022) · IEEE Access · doi:10.1109/access.2022.3160283 · Cited by 20

Exploiting deep and hand-crafted features for texture image retrieval using class membership

Yelchuri, R., Dash, J. K., Singh, P., Mahapatro, A., & Panigrahi, S. (2022) · Pattern Recognition Letters · doi:10.1016/j.patrec.2022.06.017 · Cited by 21

Feature-level ensemble approach for COVID-19 detection using chest X-ray images

Ho, T. K. K., & Gwak, J. (2022) · PLoS ONE · doi:10.1371/journal.pone.0268430 · Cited by 20

Surface soil moisture estimation from multi-frequency SAR images using ANN and experimental data on a semi-arid environment region in Konya, Turkey

Abdikan, S., Şekertekin, A., Madenoğlu, S., Özcan, H., PEKER, R. M., Pınar, M. Ö., Koc, A., Akgül, S., Secmen, H., Keçeci, M., et al. (2023) · Soil and Tillage Research · doi:10.1016/j.still.2023.105646 · Cited by 20

Unveiling Key Predictors for Early Heart Attack Detection using Machine Learning and Explainable AI Technique with LIME

Paudel, P., Karna, S. K., Saud, R., Regmi, L., Thapa, T. B., & Bhandari, M. (2023) · International Conference on Networking, Systems and Security (NSysS) · doi:10.1145/3629188.3629193 · Cited by 20

Dyslexic Children: The Need for Parents Awareness

Rauf, A. A. A., Ismail, M. A., Balakrishnan, V., & Haruna, K. (2018) · Journal of Education and Human Development · doi:10.15640/jehd.v7n2a12 · Cited by 20

Flower and leaf recognition for plant identification using convolutional neural network

FatihahSahidan, N., Juha, A. K., Mohammad, N., & Ibrahim, Z. (2019) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v16.i2.pp737-743 · Cited by 20

Improving Rice Productivity in Indonesia with Artificial Intelligence

Liundi, N., Darma, A. W., Gunarso, R., & Warnars, H. L. H. S. (2019) · International Conference on Cyber and IT Service Management (CITSM) · doi:10.1109/citsm47753.2019.8965385 · Cited by 20

Heart rate variability time domain features in automated prediction of diabetes in rat

Aggarwal, Y., Das, J., Mazumder, P. M., Kumar, R., & Sinha, R. K. (2020) · Physical and Engineering Sciences in Medicine · doi:10.1007/s13246-020-00950-8 · Cited by 20

Neural Network Analysis

Joshi, A., Sasumana, J., Ray, N. M., & Kaushik, V. (2021) · Springer eBooks · doi:10.1007/978-981-33-6191-1_18 · Cited by 20

Time to forge ahead: The Internet of Things for healthcare

Furtado, D. L., Gygax, A. F., Chan, C. A., & Bush, A. I. (2022) · Digital Communications and Networks · doi:10.1016/j.dcan.2022.06.007 · Cited by 22

Artificial Neural Network Model in Stroke Diagnosis

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