Cited by 905Artificial Intelligence in HealthcareNeural Networks and ApplicationsArtificial Intelligence in Healthcare and Education

The DOI matches its Crossref record; 56 of the 58 references it cites were found in a registry or an index.

Artificial neural networks in medical diagnosis

Open access

Amato, Filippo, López, Alberto, Peña-Méndez, Eladia María, Vaňhara, Petr, Hampl, Aleš, Havel, Josef·2013·Journal of Applied Biomedicine

DOI 10.2478/v10136-012-0031-x

Open this paper in CoChat — free account Open PDF

Abstract

An extensive amount of information is currently available to clinical specialists, ranging from details of clinical symptoms to various types of biochemical data and outputs of imaging devices. Each type of data provides information that must be evaluated and assigned to a particular pathology during the diagnostic process. To streamline the diagnostic process in daily routine and avoid misdiagnosis, artificial intelligence methods (especially computer aided diagnosis and artificial neural networks) can be employed. These adaptive learning algorithms can handle diverse types of medical data and integrate them into categorized outputs. In this paper, we briefly review and discuss the philosophy, capabilities, and limitations of artificial neural networks in medical diagnosis through selected examples.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.2478/v10136-012-0031-x

Cites

56 of 56 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

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

Show all 58 references

Research works printed but not linked: 5

10.1371/journal.pone.0044587.t004 · DOI
10.1002/1097-0142(20010415)91:8+%3C1615::AID-CNCR1175%3E3.0.CO;2-L · DOI
Gannous AS, 2011, Improving an Artificial Neural Network Model to Predict Thyroid Bending Protein Diagnosis Using Preprocessing Techniques, WASET, 50, 124
10.1371/journal.pone.0029179.t004 · DOI
10.1007/s00234-011-0886-7 · DOI

Sorted by citations

Items 101–200 of 905

Fuzzy Expert Systems (FES) for Medical Diagnosis
SushilSikchi, S., Sikchi, S., & S., A. M. (2013) · International Journal of Computer Applications · doi:10.5120/10508-5466 · Cited by 65
Age and gender classification using brain–computer interface
Kaur, B., Singh, D., & Roy, P. P. (2018) · Neural Computing and Applications · doi:10.1007/s00521-018-3397-1 · Cited by 64
Label-free surface-enhanced Raman spectroscopy with artificial neural network technique for recognition photoinduced DNA damage
Guselnikova, O., Trelin, A., Skvortsova, A., Ulbrich, P., Постников, П. С., Pershina, A. G., Sýkora, D., Švorčı́k, V., & Lyutakov, O. (2019) · Biosensors and Bioelectronics · doi:10.1016/j.bios.2019.111718 · Cited by 64
Perceptions of undergraduate medical students on artificial intelligence in medicine: mixed-methods survey study from Palestine
Jebreen, K., Radwan, E., Kammoun-Rebai, W., Alattar, E., Radwan, A., Safi, W., Radwan, W., & Alajez, M. (2024) · BMC Medical Education · doi:10.1186/s12909-024-05465-4 · Cited by 63
Performance Evaluation of Machine Learning Algorithms in Post-operative Life Expectancy in the Lung Cancer PatientsPreprint
Danjuma, K. J. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1504.04646 · Cited by 63
Artificial neural network predicts the need for therapeutic ERCP in patients with suspected choledocholithiasis
Jovanović, P., Salkic, N. N., & Zerem, E. (2014) · Gastrointestinal Endoscopy · doi:10.1016/j.gie.2014.01.023 · Cited by 62
Artificial intelligence in the pharmaceutical sector: current scene and future prospect
Kalyane, D., Sanap, G., Paul, D., Shenoy, S., Anup, N., Polaka, S., Tambe, V., & Tekade, R. K. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-814455-8.00003-7 · Cited by 62
Diagnosis of diabetes diseases using optimized fuzzy rule set by grey wolf optimization
Shankar, G. S., & Manikandan, K. (2019) · Pattern Recognition Letters · doi:10.1016/j.patrec.2019.06.005 · Cited by 61
Artificial Neural Networks to Assess Virtual Reality Anterior Cervical Discectomy Performance
Mirchi, N., Bissonnette, V., Ledwos, N., Winkler-Schwartz, A., Yilmaz, R., Karlık, B., & Maestro, R. F. D. (2019) · Operative Neurosurgery · doi:10.1093/ons/opz359 · Cited by 70
Dynamic ElecTronic hEalth reCord deTection (DETECT) of individuals at risk of a first episode of psychosis: a case-control development and validation study
Raket, L. L., Jaskolowski, J., Kinon, B. J., Brasen, J. C., Jönsson, L., Wehnert, A., & Fusar-Poli, P. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30024-8 · Cited by 60
A Review of Epidemic Forecasting Using Artificial Neural Networks
Datilo, P. M., Ismail, Z., & Jayeola, D. (2019) · International journal of epidemiologic research · doi:10.15171/ijer.2019.24 · Cited by 60
Big Data Analytics of IoT-Based Cloud System Framework: Smart Healthcare Monitoring Systems
Awotunde, J. B., Jimoh, R. G., Ogundokun, R. O., Misra, S., & Abikoye, O. C. (2022) · Internet of things · doi:10.1007/978-3-030-80821-1_9 · Cited by 58
Integrated Fault Detection Framework for Classifying Rotating Machine Faults Using Frequency Domain Data Fusion and Artificial Neural Networks
Luwei, K. C., Yunusa‐Kaltungo, A., & Sha’aban, Y. A. (2018) · Machines · doi:10.3390/machines6040059 · Cited by 58
Review on plantar data analysis for disease diagnosis
Ramirez-Bautista, J. A., Zavala, A. H., Chaparro-Cárdenas, S. L., & Huerta-Ruelas, J. A. (2018) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2018.02.004 · Cited by 58
Plasma extracellular vesicles tau and β‐amyloid as biomarkers of cognitive dysfunction of Parkinson's disease
Chung, C., Chan, L., Chen, J., Bamodu, O. A., Chiu, H., & Hong, C. (2021) · The FASEB Journal · doi:10.1096/fj.202100787r · Cited by 55
Dynamic structural neural network
Giap, C. N., Sơn, L. H., & Chiclana, F. (2018) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-171947 · Cited by 54
Machine Learning based System for Prediction of Breast Cancer Severity
Laghmati, S., Tmiri, A., & Cherradi, B. (2019) · International Conference on Wireless Networks and Mobile Communications (WINCOM) · doi:10.1109/wincom47513.2019.8942575 · Cited by 54
A new MIMO ANFIS-PSO based NARMA-L2 controller for nonlinear dynamic systems
Al-Dunainawi, Y., Abbod, M., & Jizany, A. (2017) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2017.04.016 · Cited by 55
SCADI: A standard dataset for self-care problems classification of children with physical and motor disability
Zarchi, M. S., Bushehri, S. M. M. F., & Dehghanizadeh, M. (2018) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2018.03.003 · Cited by 54
Application of Machine Learning and Word Embeddings in the Classification of Cancer Diagnosis Using Patient Anamnesis
Magna, A. R., Allende‐Cid, H., Taramasco, C., Becerra, C., & Figueroa, R. L. (2020) · IEEE Access · doi:10.1109/access.2020.3000075 · Cited by 54
Machine learning approaches to the application of disease modifying therapy for sickle cell using classification models
Khalaf, M., Hussain, A. J., Keight, R., Al‐Jumeily, D., Fergus, P., Keenan, R., & Tso, P. (2016) · Neurocomputing · doi:10.1016/j.neucom.2016.10.043 · Cited by 53
Artificial Intelligence in Diagnosing Tuberculosis: A Review
Meraj, S. S., Yaakob, R., Azman, A., Rum, S. N. M., & Nazri, A. (2019) · International Journal on Advanced Science Engineering and Information Technology · doi:10.18517/ijaseit.9.1.7567 · Cited by 52
Linear-regression convolutional neural network for fully automated coronary lumen segmentation in intravascular optical coherence tomography
Yong, Y. L., Tan, L. K., McLaughlin, R. A., Chee, K. H., & Liew, Y. M. (2017) · Journal of Biomedical Optics · doi:10.1117/1.jbo.22.12.126005 · Cited by 52
Editors' Choice—Artificial Intelligence Based Mobile Application for Water Quality Monitoring
Gunda, N. S. K., Gautam, S. H., & Mitra, S. K. (2019) · Journal of The Electrochemical Society · doi:10.1149/2.0081909jes · Cited by 53
Computational intelligence techniques for medical diagnosis and prognosis: Problems and current developments
Shahid, A. H., & Singh, M. P. (2019) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2019.05.010 · Cited by 51
Usage and implementation of neuro-fuzzy systems for classification and prediction in the diagnosis of different types of medical disorders: a decade review
Kour, H., Manhas, J., & Sharma, V. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-020-09804-x · Cited by 50
Deep Learning Neural Modelling as a Precise Method in the Assessment of the Chronological Age of Children and Adolescents Using Tooth and Bone Parameters
Zaborowicz, M., Zaborowicz, K., Biedziak, B., & Garbowski, T. (2022) · Sensors · doi:10.3390/s22020637 · Cited by 50
Development of Machine Learning Algorithms for Prediction of 5-Year Spinal Chordoma Survival
Karhade, A. V., Thio, Q. C. B. S., Ogink, P. T., Kim, J., Lozano‐Calderón, S. A., Raskin, K. A., & Schwab, J. H. (2018) · World Neurosurgery · doi:10.1016/j.wneu.2018.07.276 · Cited by 51
Identifying people at risk of developing type 2 diabetes: A comparison of predictive analytics techniques and predictor variables
Talaei‐Khoei, A., & Wilson, J. M. (2018) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2018.08.008 · Cited by 53
Medical Expert Systems for Diagnosis of Various Diseases
Singla, J., Grover, D., & Bhandari, A. (2014) · International Journal of Computer Applications · doi:10.5120/16230-5717 · Cited by 49
The Application of Artificial Intelligence Technology in Healthcare: A Systematic Review
Alloghani, M. A., Al‐Jumeily, D., Aljaaf, A. J., Khalaf, M., Mustafina, J., & Tan, S. Y. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-38752-5_20 · Cited by 49
Computer-aided diagnosis systems for osteoporosis detection: a comprehensive survey
Wani, I. M., & Arora, S. (2020) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-020-02171-3 · Cited by 52
AI-enabled digital identity – inputs for stakeholders and policymakers
Mir, U., Kar, A., & Gupta, M. (2021) · Journal of Science and Technology Policy Management · doi:10.1108/jstpm-09-2020-0134 · Cited by 34
Quality-by-design in pharmaceutical development: From current perspectives to practical applications
Kovács, B., Péterfi, O., Kovács-Deák, B., Székely-Szentmiklósi, I., FÜLÖP, I. Z., Bába, L.-I., & Boda, F. (2021) · Acta Pharmaceutica · doi:10.2478/acph-2021-0039 · Cited by 50
Physics-Informed Neural Networks in Materials Modeling and Design: A Review
Michaloglou, A., Papadimitriou, I., Gialampoukidis, I., Vrochidis, S., & Kompatsiaris, I. Y. (2025) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-025-10448-9 · Cited by 52
Toward an optimal use of artificial intelligence techniques within a clinical decision support system
Aljaaf, A. J., Al‐Jumeily, D., Hussain, A. J., Fergus, P., Al-Jumaily, M., & Abdel‐Aziz, K. (2015) · Science and Information Conference · doi:10.1109/sai.2015.7237196 · Cited by 48
Pattern recognition for electroencephalographic signals based on continuous neural networks
Alfaro-Ponce, M., Argüelles-Cruz, A.-J., & Chaírez, I. (2016) · Neural Networks · doi:10.1016/j.neunet.2016.03.004 · Cited by 48
Application of Adaptive Neuro-Fuzzy Inference System for diabetes classification and prediction
Geman, O., Chiuchisan, I., & Toderean, R. (2017) · E-Health and Bioengineering Conference (EHB) · doi:10.1109/ehb.2017.7995505 · Cited by 48
Methods for predicting vaccine immunogenicity and reactogenicity
Gonzalez‐Dias, P., Lee, E. K., Sorgi, S., de Lima, D. S., Urbanski, A. H., Silveira, E. L. V., & Nakaya, H. I. (2019) · Human Vaccines & Immunotherapeutics · doi:10.1080/21645515.2019.1697110 · Cited by 48
Evaluation of computationally intelligent techniques for breast cancer diagnosis
Kumar, V. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-05204-y · Cited by 48
Applying Multi-CNNs model for detecting abnormal problem on chest x-ray images
Nguyen, P. K., Tran, H. S., Lê, T. H., Le, T., & Nguyen, T. T. (2018) · International Conference on Knowledge and Systems Engineering/Proceedings · doi:10.1109/kse.2018.8573404 · Cited by 47
Idiosyncratic Drug-Induced Liver Injury (DILI) and Herb-Induced Liver Injury (HILI): Diagnostic Algorithm Based on the Quantitative Roussel Uclaf Causality Assessment Method (RUCAM)
Teschke, R., & Danan, G. (2021) · Diagnostics · doi:10.3390/diagnostics11030458 · Cited by 45
Discriminative feature selection for breast abnormality detection and accurate classification of thermograms
Gogoi, U. R., Bhowmik, M. K., Ghosh, A. K., Bhattacharjee, D., & Majumdar, G. (2017) · International Conference on Innovations in Electronics, Signal Processing and Communication (IESC) · doi:10.1109/iespc.2017.8071861 · Cited by 46
Diagnostic Strategies for Breast Cancer Detection: From Image Generation to Classification Strategies Using Artificial Intelligence Algorithms
Basurto-Hurtado, J. A., Cruz-Albarrán, I. A., Toledano‐Ayala, M., Ibarra-Manzano, M.-A., Morales-Hernandez, L. A., & Perez-Ramirez, C. A. (2022) · Cancers · doi:10.3390/cancers14143442 · Cited by 48
Antimicrobial Resistance and Machine Learning: Challenges and Opportunities
Elyan, E., Hussain, A., Sheikh, A., Elmanama, A. A., Vuttipittayamongkol, P., & Hijazi, K. (2022) · IEEE Access · doi:10.1109/access.2022.3160213 · Cited by 46
Design flood estimation using extreme Gradient Boosting-based on Bayesian optimization
Jarajapu, D. C., Rathinasamy, M., Agarwal, A., & Bronstert, A. (2022) · Journal of Hydrology · doi:10.1016/j.jhydrol.2022.128341 · Cited by 45
Hybrid Firefly-Bat Optimized Fuzzy Artificial Neural Network Based Classifier for Diabetes Diagnosis
Gadekallu, T. R., & Neelu, N. (2017) · International journal of intelligent engineering and systems · doi:10.22266/ijies2017.0831.03 · Cited by 45
Artificial neural network based prediction of postthrombolysis intracerebral hemorrhage and death
Chung, C., Chan, L., Bamodu, O. A., Hong, C., & Chiu, H. (2020) · Scientific Reports · doi:10.1038/s41598-020-77546-5 · Cited by 45
Using an artificial neural network to predict traumatic brain injury
Hale, A. T., Stonko, D. P., Lim, J., Guillamondegui, O. D., Shannon, C. N., & Patel, M. B. (2018) · Journal of Neurosurgery Pediatrics · doi:10.3171/2018.8.peds18370 · Cited by 48
Applications of Neural Networks in Biomedical Data Analysis
Weiss, R., Karimijafarbigloo, S., Roggenbuck, D., & Rödiger, S. (2022) · Biomedicines · doi:10.3390/biomedicines10071469 · Cited by 51
Intelligent systems for sitting posture monitoring and anomaly detection: an overview
Vermander, P., Mancisidor, A., Cabanes, I., & Pérez, N. (2024) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-024-01322-z · Cited by 43
A novel training method to preserve generalization of RBPNN classifiers applied to ECG signals diagnosis
Beritelli, F., Capizzi, G., Sciuto, G. L., Napoli, C., & Woźniak, M. (2018) · Neural Networks · doi:10.1016/j.neunet.2018.08.023 · Cited by 43
A novel empirical correlation for waterflooding performance prediction in stratified reservoirs using artificial intelligence
Kalam, S., Abu-Khamsin, S. A., Al-Yousef, H. Y., & Gajbhiye, R. N. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-05158-1 · Cited by 42
Predicting hypertension without measurement: A non-invasive, questionnaire-based approach
Wang, A., An, N., Chen, G., Li, L., & Alterovitz, G. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2015.06.012 · Cited by 42
EMR-based medical knowledge representation and inference via Markov random fields and distributed representation learning
Zhao, C., Jiang, J., Guan, Y., Guo, X., & He, B. (2018) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2018.03.005 · Cited by 41
Error-correction learning for artificial neural networks using the Bayesian paradigm. Application to automated medical diagnosis
Belciug, S., & Gorunescu, F. (2014) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2014.07.013 · Cited by 41
Aid decision algorithms to estimate the risk in congenital heart surgery
Fernández, D. R., Torra, A. M., Soriano-Payá, A., Marín-Alonso, O., & Triana-Palencia, E. (2016) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2015.12.021 · Cited by 41
A comparison between extreme learning machine and artificial neural network for remaining useful life prediction
Yang, Z., Baraldi, P., & Zio, E. (2016) · Prognostics and System Health Management Conference · doi:10.1109/phm.2016.7819794 · Cited by 41
Toxic crystal violet dye removal by novel, eco-friendly seablite biochar–ferrite composite: adsorption isotherm, kinetics, and artificial neural network
Mehta, D., Dave, P. N., & Kumar, V. (2025) · RSC Advances · doi:10.1039/d5ra04604a · Cited by 41
1H NMR- based metabolomics approaches as non- invasive tools for diagnosis of endometriosis
Ghazi, N., Arjmand, M., Akbari, Z., Mellati, A. O., Sahebkashaf, H., & Zamani, Z. (2016) · International Journal of Reproductive BioMedicine (IJRM) · doi:10.29252/ijrm.14.1.1 · Cited by 40
Artificial Intelligence in Radiology Residency Training
Forney, M. C., & McBride, A. F. (2020) · Seminars in Musculoskeletal Radiology · doi:10.1055/s-0039-3400270 · Cited by 41
Heart rate variability features from nonlinear cardiac dynamics in identification of diabetes using artificial neural network and support vector machine
Aggarwal, Y., Das, J., Mazumder, P. M., Kumar, R., & Sinha, R. K. (2020) · Journal of Applied Biomedicine · doi:10.1016/j.bbe.2020.05.001 · Cited by 41
Analysis of Unconfined Compressive Strength of Rammed Earth Mixes Based on Artificial Neural Network and Statistical Analysis
Mustafa, Y. M. H., Zami, M. S., Al‐Amoudi, O. S. B., Al-Osta, M. A., & Wudil, Y. S. (2022) · Materials · doi:10.3390/ma15249029 · Cited by 41
Survey of Fuzzy Min–Max Neural Network for Pattern Classification Variants and Applications
Sayaydeh, O. N. A., Mohammed, M. F., & Lim, C. P. (2018) · IEEE Transactions on Fuzzy Systems · doi:10.1109/tfuzz.2018.2865950 · Cited by 40
Exploring the Ability of Electronic Nose Technology to Recognize Interstitial Lung Diseases (ILD) by Non-Invasive Breath Screening of Exhaled Volatile Compounds (VOC): A Pilot Study from the European IPF Registry (eurIPFreg) and Biobank
Krauss, E., Haberer, J., Maurer, O., Barreto, G., Drakopanagiotakis, F., Degen, M., Seeger, W., & Günther, A. (2019) · Journal of Clinical Medicine · doi:10.3390/jcm8101698 · Cited by 39
Data Mining in Healthcare for Heart Diseases
Shafique, U., Majeed, F., Qaiser, H., & Mustafa, I. U. (2015) · International journal of innovation and applied studies · Cited by 38
A novel medical decision support system based on fuzzy cognitive maps enhanced by intuitive and learning capabilities for modeling uncertainty
Amirkhani, A., Papageorgiou, E. I., Mosavi, M. R., & Mohammadi, K. (2018) · Applied Mathematics and Computation · doi:10.1016/j.amc.2018.05.032 · Cited by 38
Prediction of perioperative transfusions using an artificial neural network
Walczak, S., & Velanovich, V. (2020) · PLoS ONE · doi:10.1371/journal.pone.0229450 · Cited by 42
Health and Disease Are Dynamic Complex-Adaptive States Implications for Practice and Research
Sturmberg, J. P. (2021) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2021.595124 · Cited by 39
Advances in Photonic Devices Based on Optical Phase-Change Materials
Wang, X., Qi, H., Hu, X., Yu, Z., Ding, S., Du, Z., & Gong, Q. (2021) · Molecules · doi:10.3390/molecules26092813 · Cited by 38
Computational Acceleration of Topology Optimization Using Parallel Computing and Machine Learning Methods – Analysis of Research Trends
Maksum, Y., Amirli, A., Amangeldi, A., Inkarbekov, M., Ding, Y., Romagnoli, A., Rustamov, S., & Akhmetov, B. (2022) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2022.100352 · Cited by 38
Deep Learning-Based Multilevel Classification of Alzheimer’s Disease Using Non-invasive Functional Near-Infrared Spectroscopy
Ho, T. K. K., Kim, M., Jeon, Y., Kim, B. C., Kim, J. G., Lee, K. H., Song, J., & Gwak, J. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.810125 · Cited by 38
Content-Based Image Retrieval: A Survey on Local and Global Features Selection, Extraction, Representation, and Evaluation Parameters
Srivastava, D., Singh, S. S., Rajitha, B., Verma, M., Kaur, M., & Lee, H.-N. (2023) · IEEE Access · doi:10.1109/access.2023.3308911 · Cited by 41
Combining clustering and classification ensembles: A novel pipeline to identify breast cancer profiles
Agrawal, U., Soria, D., Wagner, C., Garibaldi, J. M., Ellis, I. O., Bartlett, J. M. S., Cameron, D., Rakha, E. A., & Green, A. R. (2019) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2019.05.002 · Cited by 37
Tomada de decisão em (bio)ética clínica: abordagens contemporâneas
Motta, L. C. D. S., de Oliveira, L. N., Silva, E., & Siqueira‐Batista, R. (2016) · Revista Bioética · doi:10.1590/1983-80422016242131 · Cited by 36
Performance Evaluation of Levenberg-Marquardt Technique in Error Reduction for Diabetes Condition Classification
Khan, N., Gaurav, D., & Kandl, T. (2013) · Procedia Computer Science · doi:10.1016/j.procs.2013.05.455 · Cited by 37
Learning and inference in knowledge-based probabilistic model for medical diagnosis
Jiang, J., Li, X., Zhao, C., Guan, Y., & Yu, Q. (2017) · Knowledge-Based Systems · doi:10.1016/j.knosys.2017.09.030 · Cited by 36
The Design and Implementation of Cardiotocography Signals Classification Algorithm Based on Neural Network
Tang, H., Wang, T., Li, M., & Yang, X. (2018) · Computational and Mathematical Methods in Medicine · doi:10.1155/2018/8568617 · Cited by 36
IoT-Based Diseases Prediction and Diagnosis System for Healthcare
Vanani, I. R., & Amirhosseini, M. (2020) · Studies in big data · doi:10.1007/978-981-15-4112-4_2 · Cited by 36
Skin diseases diagnosis using artificial neural networks
Filimon, D.-M., & Albu, A. (2014) · IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI) · doi:10.1109/saci.2014.6840059 · Cited by 36
A comprehensive deep learning method for empirical spectral prediction and its quantitative validation of nano-structured dimers
Verma, S., Chugh, S., Ghosh, S., & Rahman, B. M. A. (2023) · Scientific Reports · doi:10.1038/s41598-023-28076-3 · Cited by 35
Artificial intelligence modelling to assess the risk of cardiovascular disease in oncology patients
Al-Droubi, S. S., Jahangir, E., Kochendorfer, K. M., Krive, M., Laufer‐Perl, M., Gilon, D., Okwuosa, T. M., Gans, C. P., Arnold, J. H., Bhaskar, S., et al. (2023) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztad031 · Cited by 35
Rapid discrimination of multiple myeloma patients by artificial neural networks coupled with mass spectrometry of peripheral blood plasma
Deulofeu, M., Kolářová, L., Salvadó, V., Peña‐Méndez, E. M., Almáši, M., Štork, M., Pour, L., Boadas‐Vaello, P., Ševčı́ková, S., Havel, J., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-44215-1 · Cited by 35
Self-citation
Regional Flood Frequency Analysis Using an Artificial Neural Network Model
Kordrostami, S., Alim, M. A., Karim, F., & Rahman, A. (2020) · Geosciences · doi:10.3390/geosciences10040127 · Cited by 35
Unravelling the Potential of Salivary Volatile Metabolites in Oral Diseases. A Review
Pereira, J. A. M., Porto-Figueira, P., Taware, R., Sukul, P., Rapole, S., & Câmara, J. S. (2020) · Molecules · doi:10.3390/molecules25133098 · Cited by 35
Exhaled volatile organic compounds analysis in clinical pediatrics: a systematic review
Martínez, R. A. S., Pastor, J. M., Yanes, Ó., Díaz, M. C., Puente, T. D. D., & Vinaixa, M. (2020) · Pediatric Research · doi:10.1038/s41390-020-01116-8 · Cited by 35
Hemochromatosis: Ferroptosis, ROS, Gut Microbiome, and Clinical Challenges with Alcohol as Confounding Variable
Teschke, R. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25052668 · Cited by 34
Diagnosis of Cardiovascular Diseases with Bayesian Classifiers
Fakhr, M. A., & Elsayad, A. M. (2015) · Journal of Computer Science · doi:10.3844/jcssp.2015.274.282 · Cited by 34
Examination of the ECG signal classification technique DEA-ELM using deep convolutional neural network features
Di̇ker, A., Sönmez, Y., Özyurt, F., Avcı, E., & Avcı, D. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-10517-8 · Cited by 34
Prediction model for the risk of osteoporosis incorporating factors of disease history and living habits in physical examination of population in Chongqing, Southwest China: based on artificial neural network
Wang, Y., Wang, L., Sun, Y., Wu, M., Ma, Y., Yang, L., Chun, M., Li, Z., Hossain, M. A., & Peng, B. (2021) · BMC Public Health · doi:10.1186/s12889-021-11002-5 · Cited by 34
An Intelligent Approach for Diabetes Classification, Prediction and Description
Rashid, T. A., Abdullah, S. M., & Abdullah, R. M. (2015) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-28031-8_28 · Cited by 33
Partial Opposition-Based Particle Swarm Optimizer in Artificial Neural Network Training for Medical Data Classification
Si, T., & Dutta, R. (2019) · International Journal of Information Technology & Decision Making · doi:10.1142/s0219622019500329 · Cited by 33
Machine learning and coastal processes
Beuzen, T., & Splinter, K. D. (2020) · Elsevier eBooks · doi:10.1016/b978-0-08-102927-5.00028-x · Cited by 35
Prediction of the Energy Consumption of School Buildings
Alshibani, A. (2020) · Applied Sciences · doi:10.3390/app10175885 · Cited by 33
Detection of SARS-CoV-2 Infection in Human Nasopharyngeal Samples by Combining MALDI-TOF MS and Artificial Intelligence
Deulofeu, M., García-Cuesta, E., Peña‐Méndez, E. M., Conde, J. E., Jiménez-Romero, O., Verdú, E., Serrando, M., Salvadó, V., & Boadas‐Vaello, P. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2021.661358 · Cited by 33
Self-citation
Recognition of breathprints of lung cancer and chronic obstructive pulmonary disease using the Aeonose ® electronic nose
Krauss, E., Haberer, J., Barreto, G., Degen, M., Seeger, W., & Günther, A. (2020) · Journal of Breath Research · doi:10.1088/1752-7163/ab8c50 · Cited by 33
Classification of imbalanced data using machine learning algorithms to predict the risk of renal graft failures in Ethiopia
Mulugeta, G., Zewotir, T. T., Tegegne, A. S., Juhar, L. H., & Muleta, M. B. (2023) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-023-02185-5 · Cited by 32
AN INTRODUCTION AND REVIEW ON MACHINE LEARNING APPLICATIONS IN MEDICINE AND HEALTHCARE
Singh, P. B., Singh, S. P., & Singh, D. S. P. (2019) · IEEE Conference on Information and Communication Technology (CICT) · doi:10.1109/cict48419.2019.9066250 · Cited by 33
Machine learning-based development and validation of a scoring system for progression-free survival in liver cancer
Liu, X., Hou, Y., Wang, X., Yu, L., Wang, X., Jiang, L., & Yang, Z. (2020) · Hepatology International · doi:10.1007/s12072-020-10046-w · Cited by 32
An artificial neural network-based model to predict chronic kidney disease in aged cats
Biourge, V. C., Delmotte, S., Feugier, A., Bradley, R., McAllister, M., & Elliott, J. (2020) · Journal of Veterinary Internal Medicine · doi:10.1111/jvim.15892 · Cited by 37

Authors: Amato, F., López, A., Peña-Méndez, E. M., Vaňhara, P., Hampl, A., & Havel, J.
Affiliations: Masaryk University (CZ)
Universidad de Salamanca (ES)
ORCID: Amato, Filippo
López, Alberto
Vaňhara, Petr
Hampl, Aleš
Havel, Josef
Venue: Journal of Applied Biomedicine
Publisher: University of South Bohemia in Ceske Budejovice
Published: 2013-07-31
Volume: 11
Issue: 2
Pages: 47-58
ISSN: 1214-021X (ISSN-L)
1214-0287
DOI: 10.2478/v10136-012-0031-x
OpenAlex: W2137687977
Cited by: 898
References: 58
Citations by year: 2026: 7 · 2025: 46 · 2024: 71 · 2023: 69 · 2022: 89 · 2021: 107 · 2020: 104 · 2019: 93 · 2018: 104 · 2017: 70 · 2016: 62 · 2015: 36 · 2014: 32 · 2013: 5
FWCI: 77.96
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: Journal of Applied Biomedicine (publishedVersion)
Funders: Universidad de La Laguna
Grantová Agentura České Republiky (MSM0021627501, CZ.1.05/1.1.00/02.0123, MSM0021622430, CZ.1.05/2.1.00/03.0086, GA202/07/1669, GA104/08/0229)
European Regional Development Fund (MSM0021622430, CZ.1.05/1.100, CZ.1.07/2.3.00/20.0185, CZ.1.05/2.1.00/03.0086, CZ.1.05/1.1.00/02.0123.)
Topics: Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords: artificial neural networks, computer-aided diagnosis, diagnosis, clinical decision support, adaptive learning algorithms, misdiagnosis prevention

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Artificial neural networks in medical diagnosis

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks