Artificial neural networks in medical diagnosis

Artificial neural networks: fundamentals, computing, design, and application

Basheer, I. A., & Hajmeer, M. N. (2000) · Journal of Microbiological Methods · doi:10.1016/s0167-7012(00)00201-3 · Cited by 3,316

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

Mazurowski, M. A., Habas, P. A., Zurada, J. M., Lo, J. Y.-C., Baker, J. A., & Tourassi, G. D. (2007) · Neural Networks · doi:10.1016/j.neunet.2007.12.031 · Cited by 797

Neural networks in chemistry and drug design

Zupan, J., & Gasteiger, J. (1999) · Medical Entomology and Zoology · Cited by 718

An introduction to neural computing

(1990) · Biological Psychology · doi:10.1016/0301-0511(90)90019-s · Cited by 621

Artificial Neural Networks

Kubát, M. (2017) · Springer eBooks · doi:10.1007/978-3-319-63913-0_5 · Cited by 379

Artificial Intelligence in Medical Diagnosis

Szolovits, P., Patil, R. S., & Schwartz, W. B. (1988) · Annals of Internal Medicine · doi:10.7326/0003-4819-108-1-80 · Cited by 285

Feature selection with neural networks

Verikas, A., & Bačauskienė, M. (2002) · Pattern Recognition Letters · doi:10.1016/s0167-8655(02)00081-8 · Cited by 253

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

Tate, A. R., Underwood, J., Acosta, D., Julià‐Sapé, M., Majós, C., Moreno‐Torres, À., Howe, F. A., van der Graaf, M., Lefournier, V., Murphy, M. M., et al. (2006) · NMR in Biomedicine · doi:10.1002/nbm.1016 · Cited by 227

Artificial neural networks for diagnosis and survival prediction in colon cancer

Ahmed, F. (2005) · Molecular Cancer · doi:10.1186/1476-4598-4-29 · Cited by 223

Segmentation of multiple sclerosis lesions in MR images: a review

Mortazavi, D., Kouzani, A. Z., & Hamid Soltanian-zadeh, A. (2011) · Neuroradiology · doi:10.1007/s00234-011-0886-7 · Cited by 146

Comparative Study of Response Surface Methodology and Artificial Neural Network for Modeling and Optimization of Extraction Process Parameters on Tetrapleura Tetraptera

Oyedoh, E. A., Erumi, G. A., & Akhabue, C. E. (2020) · Journal of applied science and environmental management · doi:10.4314/jasem.v24i2.18 · Cited by 3

Prediction of lattice constants for the full-Heusler alloys by vector regression model and Artificial Neural Networks

Mehmood, N., Ahmad, R., Gul, A., Zaman, A., Murtaza, G., Ahmad, J., & Khattak, F. Y. (2021) · Computational Condensed Matter · doi:10.1016/j.cocom.2021.e00605 · Cited by 3

Diagnosing Chronic Kidney disease using Artificial Neural Network (ANN)

Rashid, A. (2023) · Journal of Information Technology and Computing · doi:10.48185/jitc.v4i1.584 · Cited by 3

An optical scheme of on-chip matrixing by phase-change based tunable weighting of photonic tensor unit

Ye, Z., Yang, J., Sun, J., Hu, B., & Zhou, S. (2023) · Journal of Physics D Applied Physics · doi:10.1088/1361-6463/aceb73 · Cited by 3

Prediction of Changes in Blood Parameters Induced by Low-Frequency Ultrasound

Ostaševičius, V., Paulauskaitė-Tarasevičienė, A., Lesauskaitė, V., Jūrėnas, V., Tatarūnas, V., Stankevıčıus, E., Tunaitytė, A., Venslauskas, M., & Kižauskienė, L. (2023) · Applied System Innovation · doi:10.3390/asi6060099 · Cited by 3

Incremental image retrieval method based on feature perception and deep hashing

Liao, K., Lin, J., Zheng, Y., Wang, K., & Wen, F. (2024) · International Journal of Multimedia Information Retrieval · doi:10.1007/s13735-024-00319-7 · Cited by 3

Automated Verification of Soundness of DNN Certifiers

Singh, A., Sarita, Y. C., Mendis, C., & Singh, G. (2025) · Proceedings of the ACM on Programming Languages · doi:10.1145/3720509 · Cited by 3

The Use of Artificial Neural Networks in Clinical Medicine

Jovanović, M., Milenković, D., Perković, M., Milenković, T., & Vuk, N. (2016) · International Scientific Conference Sinteza · doi:10.15308/sinteza-2016-112-117 · Cited by 3

Fuzzy Adaptation for Particle Swarm Optimization for Modular Neural Networks Applied to Iris Recognition

Sánchez, D., Melín, P., & Castillo, O. (2017) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-67137-6_11 · Cited by 3

Process-Monitoring-for- Quality — A Model Selection Criterion for Shallow Neural Networks

Escobar, C. A., & Morales-Menéndez, R. (2019) · Annual Conference of the PHM Society · doi:10.36001/phmconf.2019.v11i1.816 · Cited by 3

Machine learning model to characterize seizure development in traumatic brain injury patients.

Rocca, M. L., Garner, R., & Duncan, D. (2020) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2020.12.009 · Cited by 3

Designing optimizing procedures for task switching to ensure efficiency in the hospital laboratory

Kim, Y.-J., Kim, G., Kim, S., Jung, D., & Park, M. W. (2021) · Scientific Reports · doi:10.1038/s41598-021-92116-z · Cited by 3

Human Voice Recognition Using Artificial Neural Networks

Nurdiyanto, H. (2021) · Turkish Journal of Computer and Mathematics Education (TURCOMAT) · doi:10.17762/turcomat.v12i9.3363 · Cited by 3

Medical Diagnosis using Back Propagation Algorithm in ANN

Verma, M. (2014) · Cited by 3

Passive absorption prediction of transdermal drug application with Artificial Neural Network

Terzić, V., Tarakčija, A., Vardo, A., Hadžajlić, A., Šakić, V., Smajlović, S., Milisav, A., Midžić, E., Smajović, A., & Dedić, M. (2017) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-981-10-4166-2_113 · Cited by 3

Diagnostic Rule Extraction Using the Dempster-Shafer Theory Extended for Fuzzy Focal Elements

Porębski, S., & Straszecka, E. (2017) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-59162-9_7 · Cited by 3

Forecasting model of a movie's profitability

Galvão, M., & Henriques, R. (2018) · Iberian Conference on Information Systems and Technologies (CISTI) · doi:10.23919/cisti.2018.8399184 · Cited by 3

Diabetes Diagnosis System Based on Support Vector Machines Trained by Vortex Optimization Algorithm

Çankaya, Ş. F., Çankaya, İ. A., Yi̇ği̇t, T., & Koyun, A. (2018) · Advances in bioinformatics and biomedical engineering book series · doi:10.4018/978-1-5225-4769-3.ch009 · Cited by 3

Diagnostic classification of undifferentiated fevers using artificial neural network

Vasudeva, S. T., Rao, S. S., Panambur, N. K., Mahabala, C., Dakappa, P. H., & Prasad, K. (2020) · AIP conference proceedings · doi:10.1063/5.0007749 · Cited by 3

The relationship between neuropsychological function and responsiveness to vitamin D supplementation using artificial neural networks

Allahyari, E., Hanachi, P., Ariakia, F., Kashfi, T. E., Ferns, G. A., Bahrami, A., & Ghayour‐Mobarhan, M. (2020) · Nutrition and Health · doi:10.1177/0260106020937190 · Cited by 4

A Powerful Holonic and Multi-Agent-Based Front-End for Medical Diagnostics Systems

Akbari, Z., & Unland, R. (2021) · Intelligent systems reference library · doi:10.1007/978-3-030-79161-2_13 · Cited by 3

Diagnosis of Breast Cancer Using Intelligent Information Systems Techniques

Al-Khasawneh, A. (2016) · IGI Global eBooks · doi:10.4018/978-1-5225-0788-8.ch010 · Cited by 3

Machine Learning–Enhanced Decision-Making

Rybak, N., & Hassall, M. E. (2022) · Springer eBooks · doi:10.1007/978-3-030-84205-5_20 · Cited by 3

Comparison of segmentation methods for the detection of breast cancer using thermal images

Lennox, N., & Haskins, B. (2019) · International Multidisciplinary Information Technology and Engineering Conference (IMITEC) · doi:10.1109/imitec45504.2019.9015836 · Cited by 3

Machine Learning Is the Future for Lung Cancer Prognosis and Prediction

Ayshwarya, B., & Senthil, S. T. (2020) · Advances in medical technologies and clinical practice book series · doi:10.4018/978-1-7998-2101-4.ch011 · Cited by 3

Modeling and Survival Analysis of Breast Cancer: A Statistical, Artificial Neural Network, and Decision Tree Approach

Mudunuru, V. R. (2016) · Digital Commons - University of South Florida (University of South Florida) · Cited by 3

Machine Learning for Data-driven Prognostics : Methods and Applications

Diez-Olivan, A. (2017) · doi:10.20868/upm.thesis.48053 · Cited by 3

Could Deep Learning Change Our Working Lives?

Sabharwal, N. K. (2018) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2018.02.010 · Cited by 3

Complexity Analysis of Approaching Clinical Psychiatry with Predictive Analytics and Neural NetworksPreprint

Hossain, S. Q. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.12471 · Cited by 3

Power and Challenges of Big Data: Why Clinical Researchers Should Not Be Ignored

Poole, D. (2019) · Journal of Neurosurgical Anesthesiology · doi:10.1097/ana.0000000000000658 · Cited by 3

Automatic Diagnosing of Infant Hip Dislocation Based on Neural Network

Yu, X., Lin, D., Lan, W., Zhong, B., & Lv, P. (2018) · International Symposium on Image Computing and Digital Medicine (ISICDM) · doi:10.1145/3285996.3286021 · Cited by 2

An Efficient Artificial Neural Network for Coronary Heart Disease Prediction

Sheta, P. V. (2021) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2021.39559 · Cited by 2

An Enhanced Modular-Based Neural Network Framework for Effective Medical Diagnosis

Fraser, E. A. (2024) · Multimedia Research · doi:10.46253/j.mr.v7i2.a1 · Cited by 2

Deep Learning for Osteoporosis Diagnosis Using Magnetic Resonance Images of Lumbar Vertebrae

Mousavinasab, S.-M., Hedyehzadeh, M., & Nasab, S. T. M. (2025) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-025-01547-x · Cited by 2

Optimizing biofortified crop selection: a novel feed-backward double hierarchy linguistic neural network approach with Yager-Dombi t-norms

Abosuliman, S. S., Abdullah, S., & Ali, N. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11154-5 · Cited by 2

Empowering Self-Care: Heart Attack Predictions Using Explainable AI and Machine Learning

Chennupati, N., Maddineni, S. R., Penumuchu, C., Vemuganti, H. V. R., Guduri, M., Veeravalli, S. D., & Thota, S. K. (2025) · International Conference on Artificial Intelligence, Blockchain, and Internet of Things (AIBThings) · doi:10.1109/aibthings66987.2025.11296266 · Cited by 2

Investigation of the Performance of Artificial Neural Networks Using Different Learning Algorithms in Biomedicine Datasets

Ulaş, M., Altay, O., & Altay, E. V. (2025) · IEEE Access · doi:10.1109/access.2025.3648517 · Cited by 2

A Software Solution for Preventing Alzheimer's Disease Based on MapReduce Framework

Zettam, M., Laassiri, J., & Enneya, N. (2017) · IEEE International Conference on Information Reuse and Integration (IRI) · doi:10.1109/iri.2017.77 · Cited by 2

Quantitative assessment of distant recurrence risk in early stage breast cancer using a nonlinear combination of pathological, clinical and imaging variables

Nichols, B., Chelales, E. M., Wang, R., Schulman, A., Gallagher, J., Greenup, R. A., Geradts, J., Harter, J. M., Marcom, P. K., Wilke, L. G., et al. (2020) · Journal of Biophotonics · doi:10.1002/jbio.201960235 · Cited by 2

Artificial Neural Network Research in Online Social Networks

Walczak, S. M. (2021) · IGI Global eBooks · doi:10.4018/978-1-6684-2408-7.ch004 · Cited by 2

Artificial Intelligence in Public Health: Facts and Hyperboles

Devgun, P., & Sadana, P. (2023) · AMEI's Current Trends in Diagnosis & Treatment · doi:10.5005/jp-journals-10055-0154 · Cited by 2

Recent Trends in Robotic Process Automation

Kannadhasan, S., Nagarajan, R., & Venusamy, K. (2024) · Advances in higher education and professional development book series · doi:10.4018/979-8-3693-0343-6.ch016 · Cited by 2

The Role of Artificial Intelligence in Reshaping Human Resources in Healthcare Industry: Application and Challenges

Saloni, S., Gupta, N., Kamalpreet, Ghosh, M., & Agarwal, A. K. (2023) · International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI) · doi:10.1109/icaiihi57871.2023.10489804 · Cited by 3

Impact of Artificial Intelligence in the Health Sector

Lal, S., Raghav, A. S., Arora, M. K., Mishra, S., & Singh, B. (2024) · Advances in electronic government, digital divide, and regional development book series · doi:10.4018/979-8-3693-5976-1.ch005 · Cited by 2

Machine learning-driven prediction of optical responses and inverse design of annular aperture arrays

Chen, Y., Feng, F., Wen, X., Wang, Z., & Fu, Y. (2025) · Journal of Applied Physics · doi:10.1063/5.0276411 · Cited by 3

Healthcare 5.0: Enabling Technologies and Potential Use Cases

Sahu, M., Maurya, P., & Krishnaveni, S. (2025) · Springer eBooks · doi:10.1007/978-3-031-87837-4_13 · Cited by 2

A Mobile-based Neuro-fuzzy System for Diagnosing and Treating Cardiovascular Diseases

Isinkaye, F. O., Soyemi, J., & Oluwafemi, O. P. (2017) · International Journal of Information Engineering and Electronic Business · doi:10.5815/ijieeb.2017.06.03 · Cited by 2

Diagnosis of Disease Using Feature Decimation with Multiple Classifier System

Tajanpure, R., & Jena, S. (2017) · Advances in intelligent systems and computing · doi:10.1007/978-981-10-5520-1_7 · Cited by 2

HEART DISEASES DETECTION FROM NOISY RECORDINGS OF SMARTPHONE DEVICES

Ho, W., Chen, Y. J., Zhang, Y., Tao, Y., & Kuo, H.-W. H. (2018) · Journal of Mechanics in Medicine and Biology · doi:10.1142/s0219519418500392 · Cited by 2

Region Specific Weight Measuring System for Bedridden patients

Nancy, G. A., Rashmi, B., & Kalpana, R. (2020) · International Conference on Bio Signals, Images, and Instrumentation (ICBSII) · doi:10.1109/icbsii49132.2020.9167606 · Cited by 2

Fuzzy classifiers in cardiovascular disease diagnostics: Review

Hodashinsky, I. A. (2020) · Сибирский журнал клинической и экспериментальной медицины · doi:10.29001/2073-8552-2020-35-4-22-31 · Cited by 2

Gradient Regularization as Approximate Variational Inference

Unlu, A., & Aitchison, L. (2021) · Entropy · doi:10.3390/e23121629 · Cited by 2

APPLYING AI FOR TIMELY INPUT TO A SMART HEALTHCARE SYSTEM

Rizk, D. K. A. A., Hosny, H. M., El-Horbaty, E.-S. M., & Salem, A. M. (2022) · Journal of Southwest Jiaotong University · doi:10.35741/issn.0258-2724.57.4.28 · Cited by 2

Artificial intelligence in medicine: its working, potentials and challenges

Goel, R. (2022) · International Journal of Advances in Medicine · doi:10.18203/2349-3933.ijam20223412 · Cited by 2

Intelligent System of Diagnosing and Predicting Cardiovascular Diseases

Khidirova, C., Ruzibaev, O. B., & Shoimova, S. (2022) · International Conference on Information Science and Communications Technologies (ICISCT) · doi:10.1109/icisct55600.2022.10146803 · Cited by 2

Using Data Mining Techniques to Predict Chronic Kidney Disease: A Review Study

Sattari, M. A., & Mohammadi, M. (2023) · International Journal of Preventive Medicine · doi:10.4103/ijpvm.ijpvm_482_21 · Cited by 2

Building Trust and Safety in Artificial Intelligence with Abstract Interpretation

Singh, G. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-44245-2_3 · Cited by 2

Artificial Intelligence for Global Health: Difficulties and Challenges: A Narrative Review

Sadat, N., Shuttari, M. S., & Sadat‐Ali, M. (2024) · Open Journal of Epidemiology · doi:10.4236/ojepi.2024.141009 · Cited by 3

A numerical solution of Schrödinger equation for the dynamics of early universe

Mughal, M. Z., & Khan, A. (2024) · Astronomy and Computing · doi:10.1016/j.ascom.2024.100894 · Cited by 4

Defining the rehabilitation treatment programs for stroke patients by applying Neural Network and Decision Trees models

Prasertsakul, T., Kaimuk, P., & Charoensuk, W. (2014) · Biomedical Engineering International Conference · doi:10.1109/bmeicon.2014.7017422 · Cited by 2

Development of Artificial Neural Network for body composition Analysis

Jadhav, N. V., & Bagal, P. U. (2014) · IOSR Journal of Electronics and Communication Engineering · doi:10.9790/2834-0955100107 · Cited by 2

Swarm intelligence applied to the risk evaluation for congenital heart surgery

Zapata-Impata, B. S., Fernández, D. R., & Torra, A. M. (2015) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2015.7318338 · Cited by 2

Comparison of NEAT and Backpropagation Neural Network on Breast Cancer Diagnosis

Turabieh, H. (2016) · International Journal of Computer Applications · doi:10.5120/ijca2016909245 · Cited by 2

Automatic Identification of Security Risks in Edicts for Software Procurement

Peclat, R. N., & Ramos, G. N. (2016) · Brazilian Conference on Intelligent Systems · doi:10.1109/bracis.2016.057 · Cited by 2

Predicting mortality following gastrointestinal surgery

Sapra, R. L., Mehrotra, S., Lalwani, S., Mangla, V., Mehta, N. N., & Nundy, S. (2017) · Current Medicine Research and Practice · doi:10.1016/j.cmrp.2017.12.001 · Cited by 2

A General Medical Diagnosis System Formed by Artificial Neural Networks and Swarm Intelligence Techniques

Vasant, P. (2018) · Advances in bioinformatics and biomedical engineering book series · doi:10.4018/978-1-5225-4769-3.ch006 · Cited by 2

DECISION MAKING BY SIMULATION- A CASE STUDY

Gürsel, G. (2018) · Uluslararası İktisadi ve İdari Bilimler Dergisi · doi:10.29131/uiibd.405258 · Cited by 2

Early-Stage Ovarian Cancer Diagnosis Using Fuzzy Rough Sets with SVM Classification

Shoaip, N., Elmogy, M. M., Riad, A. M., Zaghloul, H., & Badria, F. A.-E. (2017) · Advances in computational intelligence and robotics book series · doi:10.4018/978-1-5225-2229-4.ch003 · Cited by 2

Automatic Generation of Agents using Reusable Soft Computing Code Libraries to develop Multi Agent System for Healthcare

Sajja, P. S. (2015) · International Journal of Information Technology and Computer Science · doi:10.5815/ijitcs.2015.05.07 · Cited by 2

Understanding the Role of Artificial Neural Networks in the Prediction of Mental Health Diseases

Sinhal, S., & Nanda, R. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-6014-9_46 · Cited by 2

Neural Networks Architecture for COVID-19 Early Detection

Zgheib, R., Chahbandarian, G., Kamalov, F., & Labban, O. E. (2021) · International Symposium on Networks, Computers and Communications (ISNCC) · doi:10.1109/isncc52172.2021.9615883 · Cited by 2

SuPART: supervised projective adapted resonance theory for automatic quality assurance approval of radiotherapy treatment plans

Kamran, H., Aleman, D. M., McIntosh, C., & Purdie, T. G. (2022) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ac568f · Cited by 2

IMPLICATIONS OF ARTIFICIAL INTELLIGENCE IN HEALTHCARE DELIVERY IN THE HOSPITAL SETTINGS: A LITERATURE REVEIW

Khaled, N., Turki, A., & Aidalina, M. (2019) · International Journal of Public Health and Clinical Sciences · doi:10.32827/ijphcs.6.5.22 · Cited by 2

Feature Selection in Electroencephalographic Signals Using a Multicriteria Decision Analysis Method

Acosta, A. E. G., Soto, M. D. T., Soto, A. T., Sentí, E. E. P. D. L., & Zezzatti, C. A. O. O. (2023) · EAI/Springer Innovations in Communication and Computing · doi:10.1007/978-3-031-29775-5_14 · Cited by 2

Using Counterexamples to Improve Robustness Verification in Neural Networks

Afzal, M., Gupta, A., & Akshay, S. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-45329-8_20 · Cited by 2

Failure Predictability Model Based on Reliability Assessment of Prestressed Concrete Bridge Beams Using Artificial Neural Networks

Lamouri, H., Mkhalet, M. E., & Lamdouar, N. (2024) · Civil Engineering and Architecture · doi:10.13189/cea.2024.121323 · Cited by 2

On Scalable Testing of Samplers

Pote, Y., & Meel, K. S. (2022) · neural information processing systems · doi:10.52202/068431-2035 · Cited by 2

Red neuronal para clasificación de riesgo en cooperativas de ahorro y crédito

Peña, M., & Orellana, J. M. (2018) · Congreso de Ciencia y Tecnología ESPE · doi:10.24133/cctespe.v13i1.710 · Cited by 2

A MapReduce-based Adjoint method for preventing brain disease

Zettam, M., Laassiri, J., & Enneya, N. (2018) · Journal Of Big Data · doi:10.1186/s40537-018-0136-5 · Cited by 2

Bin-BB: Binning with Branch & Bound feature selection for improved diabetes classification

Cheruku, R., & Edla, D. R. (2017) · IEEE India Conference · doi:10.1109/indicon.2017.8487868 · Cited by 2

A Brief View on Medical Diagnosis Applications with Deep Learning

Köse, U., Deperlioğlu, Ö., Alzubi, J. A. A., & Pătruţ, B. (2020) · Studies in computational intelligence · doi:10.1007/978-981-15-6325-6_3 · Cited by 2

A Hybrid Medical Diagnosis Approach with Swarm Intelligence Supported Autoencoder Based Recurrent Neural Network System

Köse, U., Deperlioğlu, Ö., Alzubi, J. A. A., & Pătruţ, B. (2020) · Studies in computational intelligence · doi:10.1007/978-981-15-6325-6_7 · Cited by 2

Diagnosis Ranking with Knowledge Graph Convolutional Networks

Liu, B., Zuccon, G., Hua, W., & Chen, W. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-72113-8_24 · Cited by 2

Application of Artificial Intelligence Techniques to Predict Effects of Cigarette Smoking on Hematological Parameters and Attributable Diseases

Nejra, K., Lejla, K., Nermana, K., Amina, K., Božana, L., Amina, L., & Badnjević, A. (2021) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-3-030-73909-6_36 · Cited by 2

Data Mining for the Prediction of Heart Disease: A Literature Survey

Umasankar, P., & Thiagarasu, V. (2019) · Asian Journal of Computer Science and Technology · doi:10.51983/ajcst-2019.8.1.2128 · Cited by 2

Innovative machine vision technique for 2D/3Dcomplex and irregular surfaces modelling

Anwar, S., Smith, L. N., & Smith, M. L. (2012) · UWE Research Repository (UWE Bristol) · Cited by 2

Decision-making in clinical (bio)ethics: contemporary approaches

Motta, L. C. D. S., de Oliveira, L. N., Silva, E., & Siqueira‐Batista, R. (2018) · Figshare · doi:10.6084/m9.figshare.7509962 · Cited by 2

Infrared Thermography for the Assessment of Lumbar Sympathetic Blocks in Patients with Complex Regional Pain Syndrome

Soriano, M. C. (2022) · RiuNet (Universitat Politècnica de València) · doi:10.4995/thesis/10251/181699 · Cited by 2

High-accuracy fine-tuned vision transformer model for diagnosing COVID-19 from chest X-ray imagesPreprint

Chen, T., Philippi, I., Phan, Q. B., Nguyen, L., daCunha, C., & Nguyen, T. T. (2024) · doi:10.36227/techrxiv.170421494.42458284/v1 · Cited by 2

A Comparative Analysis of Artificial Neural Network and Deep Learning Techniques in Heart Disease

Et.al, A. S. (2021) · Türk bilgisayar ve matematik eğitimi dergisi · doi:10.17762/turcomat.v12i3.1646 · Cited by 1

AGMC-TU Pap-Smear Cytological Image Dataset: Creation, Annotation, and Analysis towards Early Detection of Cervical Cancer

Roy, S. D., Saha, P., Nath, N., Datta, A., & Bhowmik, M. K. (2022) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi54592.2022.00018 · Cited by 1

Verifying Neural Network Controlled Systems by Combining Forward and Backward Reachability Analysis

Wang, X., Chen, L., Liu, Z., Li, M., & Yin, B. (2025) · IEEE International Conference on Software Quality, Reliability and Security (QRS) · doi:10.1109/qrs65678.2025.00040 · Cited by 1

An exponential evaluation for recurrent neural network with discrete delays

Martsenyuk, V., & Сверстюк, А. С. (2019) · System research and information technologies · doi:10.20535/srit.2308-8893.2019.2.07 · Cited by 1

Predicción de Covid19 con el uso del Algoritmo Random Forest y Redes Neuronales Artificiales

Patiño-Pérez, D., Bustillos, R. S., Mora, C. M., & Botto-Tobar, M. (2020) · Ecuadorian Science Journal · doi:10.46480/esj.4.2.41 · Cited by 1

PERBANDINGAN KLASIFIKASI ALGORITMA K-NN, NEURAL NETWORK, NAÏVE BAYES, C 4.5 UNTUK MENDETEKSI WEB PHISING

Nanda, E., Istikomah, I., Amari, N. A., & Pristyanto, Y. (2023) · Jurnal Informatika Komputer Bisnis dan Manajemen · doi:10.61805/fahma.v16i3.88 · Cited by 1

Classifying Diabetes Disease Using Feedforward MLP Neural Networks

Al-Khasawneh, A. M., & Hijazi, H. (2021) · IGI Global eBooks · doi:10.4018/978-1-6684-2408-7.ch061 · Cited by 1

A Reduced-Memory Multi-layer Perceptron with Systematic Network Weights Generated and Trained Through Distribution Hyper-parameters

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Development of Artificial Neural Network Model for Medical Specialty Recommendation

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