An Explainable Machine Learning Model for Early Detection of Parkinson's Disease using LIME on DaTSCAN Imagery

Very Deep Convolutional Networks for Large-Scale Image Recognition

Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155

ImageNet: A large-scale hierarchical image database

Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333

Rethinking the Inception Architecture for Computer Vision

Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.308 · Cited by 32,138

A Unified Approach to Interpreting Model Predictions

Lundberg, S., & Lee, S.-I. (2017) · arXiv · doi:10.48550/arxiv.1705.07874 · Cited by 23,707

A survey on transfer learning

Pan, S. J., & Yang, Q. (2010) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2009.191 · Cited by 23,387

"Why Should I Trust You?": Explaining the Predictions of Any Classifier

Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939778 · Cited by 17,555

On a Method to Measure Supervised Multiclass Model’s Interpretability: Application to Degradation Diagnosis (Short Paper)

Gauriat, C.-M., Pencolé, Y., Ribot, P., et al. (2024) · HAL · doi:10.4230/oasics.dx.2024.27 · Cited by 12,623

“Why Should I Trust You?”: Explaining the Predictions of Any Classifier

Ribeiro, M., Singh, S., & Guestrin, C. (2016) · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations · doi:10.18653/v1/n16-3020 · Cited by 6,682

Parkinson disease

Poewe, W., Seppi, K., Tanner, C. M., Halliday, G. M., Brundin, P., Volkmann, J., Schrag, A.-E., & Lang, A. E. (2017) · Nature Reviews Disease Primers · doi:10.1038/nrdp.2017.13 · Cited by 5,415

Content-based image retrieval using handcraft feature fusion in semantic pyramid

Taheri, F., Rahbar, K., & Beheshtifard, Z. (2023) · International Journal of Multimedia Information Retrieval · doi:10.1007/s13735-023-00292-7 · Cited by 15

Extensive Review of Literature on Explainable AI (XAI) in Healthcare Applications

Mariappan, R. (2024) · Recent Advances in Computer Science and Communications · doi:10.2174/0126662558296699240314055348 · Cited by 18

Analysis of Different Modality of Data to Diagnose Parkinson's Disease Using Machine Learning and Deep Learning Approaches: A Review

Arnab, S. B., Palash, M. I. A., Islam, R., Ovi, H. H., Yousuf, M. A., & Uddin, M. Z. (2024) · Expert Systems · doi:10.1111/exsy.13790 · Cited by 17

An Improved Method for Diagnosis of Parkinson’s Disease using Deep Learning Models Enhanced with Metaheuristic AlgorithmPreprint

Mallik, S., Majhi, B., Kashyap, A., Mohanty, S., Dash, S., Li, A., & Zhao, Z. (2023) · Research Square · doi:10.21203/rs.3.rs-3387953/v1 · Cited by 15

Machine learning and deep learning in clinical practice: Advancing neurodegenerative disease diagnosis with multimodal markers

Zarei, O., moghaddam, M. T., & Vastegani, S. M. (2025) · Brain Research Bulletin · doi:10.1016/j.brainresbull.2025.111667 · Cited by 21

Parkinson’s Disease Detection by Using Machine Learning Method based on Local Classification on Class Boundary

Du, Q., Shen, J., Wen, P., & Chen, X. (2024) · Discover Applied Sciences · doi:10.1007/s42452-024-06295-1 · Cited by 17

Fruits and Vegetable Diseases Recognition Using Convolutional Neural Networks

Amin, J., Anjum, M. A., Sharif, M. I., Kadry, S., & Nam, Y. (2021) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2022.018562 · Cited by 14

PD-ADSV: An automated diagnosing system using voice signals and hard voting ensemble method for Parkinson’s disease

Ghaheri, P., Shateri, A., & Nasiri, H. (2023) · Software Impacts · doi:10.1016/j.simpa.2023.100504 · Cited by 14

Interpretability of Machine Learning Methods Applied to Neuroimaging

Thibeau–Sutre, E., Collin, S., Burgos, N., & Colliot, O. (2023) · Neuromethods · doi:10.1007/978-1-0716-3195-9_22 · Cited by 13

Debiasing Counterfactuals in the Presence of Spurious Correlations

Kumar, A., Fathi, N., Mehta, R., Nichyporuk, B., Falet, J.-P. R., Tsaftaris, S. A., & Arbel, T. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-45249-9_27 · Cited by 13

Improving Object Recognition in Crime Scenes via Local Interpretable Model-Agnostic Explanations

Farhood, H., Saberi, M., & Najafi, M. (2021) · IEEE International Enterprise Distributed Object Computing Conference Workshops (EDOCW) · doi:10.1109/edocw52865.2021.00037 · Cited by 13

Long Short-Term Memory-Based Deep Learning Models for Screening Parkinson’s Disease Using Sequential Diagnostic Codes

Yoon, S., Kim, M., & Lee, W. (2023) · Journal of Clinical Neurology · doi:10.3988/jcn.2022.0160 · Cited by 15

Interoperable and explainable machine learning models to predict morbidity and mortality in acute neurological injury in the pediatric intensive care unit: secondary analysis of the TOPICC study

Munjal, N. K., Clark, R. S. B., Simon, D. W., Kochanek, P. M., & Horvat, C. M. (2023) · Frontiers in Pediatrics · doi:10.3389/fped.2023.1177470 · Cited by 12

Automated identification of uncertain cases in deep learning-based classification of dopamine transporter SPECT to improve clinical utility and acceptance

Buddenkotte, T., Apostolova, I. I., Opfer, R., Krüger, J., Klutmann, S., & Buchert, R. (2023) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-023-06566-w · Cited by 13

Automated Parkinson's Disease Detection: A Review of Techniques, Datasets, Modalities, and Open Challenges

Zadoo, S., Singh, Y. P., & Singh, P. K. (2024) · International Journal on Smart Sensing and Intelligent Systems · doi:10.2478/ijssis-2024-0008 · Cited by 13

A hybrid transposed attention based deep learning model for wearable and explainable stress recognition

Tanwar, R., Singh, G., & Pal, P. K. (2024) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2024.109551 · Cited by 12

Computer-aided cholelithiasis diagnosis using explainable convolutional neural network

Kumar, D., Mehta, M. A., Kotecha, K. V., & Kulkarni, A. (2025) · Scientific Reports · doi:10.1038/s41598-025-85798-2 · Cited by 13

Evaluation of Feature Selection Techniques for Predicting Parkinson's Disease using Machine Learning Models

Akilandeswari, A. (2025) · International Conference on Electronics and Renewable Systems (ICEARS) · doi:10.1109/icears64219.2025.10940164 · Cited by 12

Cardiac Clarity: Harnessing Machine Learning for Accurate Heart-Disease Prediction

Santhosh, S., Chadaga, K., Arjunan, R. V., & D’Souza, S. (2025) · IEEE Access · doi:10.1109/access.2025.3573760 · Cited by 12

Enhancing Healthcare Data Security and Privacy through AI-Driven Encryption and Privacy-Preserving Techniques

Ganesh, D., V, Bommi, R. M., & Nandhini, T. J. (2025) · International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI) · doi:10.1109/icdsaai65575.2025.11011747 · Cited by 12

Diagnosis of Parkinson syndrome and Lewy-body disease using 123I-ioflupane images and a model with image features based on machine learning

Nakajima, K., Saito, S., Chen, Z., Komatsu, J., Maruyama, K., Shirasaki, N., Watanabe, S., Inaki, A., Ono, K., & Kinuya, S. (2022) · Annals of Nuclear Medicine · doi:10.1007/s12149-022-01759-z · Cited by 12

A Proactive Explainable Artificial Neural Network Model for the Early Diagnosis of Thyroid Cancer

Aljameel, S. S. (2022) · Computation · doi:10.3390/computation10100183 · Cited by 12

Review of artificial intelligence clinical applications in Nuclear Medicine

Panagiotidis, E., Papachristou, K., Makridou, A., Zoglopitou, L.-A., Paschali, A. N., Kalathas, T., Chatzimarkou, M., & Chatzipavlidou, V. (2023) · Nuclear Medicine Communications · doi:10.1097/mnm.0000000000001786 · Cited by 11

Explainable Machine Learning Models for Brain Diseases: Insights from a Systematic Review

Mallma, M. J. R., Zuloaga-Rotta, L., Borja-Rosales, R., Mallma, J. R. R., Vilca-Aguilar, M., Salas-Ojeda, M., & Mauricio, D. (2024) · Neurology International · doi:10.3390/neurolint16060098 · Cited by 15

XAI-based Clinical Decision Support System: A Systematic Review

Kim, S., Kim, D., Kim, M., Ko, H., & Jeong, O. (2024) · doi:10.20944/preprints202406.0721.v1 · Cited by 72

An Empirical Study on Low- and High-Level Explanations of Deep Learning Misbehaviours

Zohdinasab, T., Riccio, V., & Tonella, P. (2023) · Proceedings of the ... ACM-IEEE International Symposium on Empirical Software Engineering and Measurement/International Symposium on Empirical Software Engineering and Measurement · doi:10.1109/esem56168.2023.10304866 · Cited by 10

Machine learning approaches for neurological disease prediction: A systematic review

Fatima, A., & Masood, S. (2024) · Expert Systems · doi:10.1111/exsy.13569 · Cited by 11

Explainable machine learning for predicting conversion to neurological disease: Results from 52,939 medical records

Felix, C., Johnston, J. D., Owen, K., Shirima, E., Hinds, S. R., Mandl, K. D., Milinovich, A., & Alberts, J. L. (2024) · Digital Health · doi:10.1177/20552076241249286 · Cited by 12

“Do not deceive me anymore!” interpretation through model design and visualization for instagram counterfeit seller account detection

Park, J., Gu, J., & Kim, H. Y. (2022) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107418 · Cited by 10

Federated Learning of Explainable Artificial Intelligence Models for Predicting Parkinson’s Disease Progression

Bárcena, J. L. C., Ducange, P., Marcelloni, F., Renda, A., & Ruffini, F. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-44064-9_34 · Cited by 10

Enabling Real-Time Prediction of In-game Deaths through Telemetry in Counter-Strike: Global Offensive

Marshall, S., Blom, P. M., & Spronck, P. H. M. (2022) · International Conference on the Foundations of Digital Games (FDG) · doi:10.1145/3555858.3555859 · Cited by 9

Analysis and Implementation of Machine Learning Model for Detection of Parkinson’s Disease

Spandan, K., Kumar, B., Banerjee, P., Jha, P., Pathak, L. K., & Dehury, M. K. (2023) · International Conference on Sustainable Computing and Data Communication Systems (ICSCDS) · doi:10.1109/icscds56580.2023.10104661 · Cited by 9

Human-Centered Explainable AI at the Edge for eHealth

Dutta, J., & Puthal, D. (2023) · IEEE International Conference on Edge Computing and Communications (EDGE) · doi:10.1109/edge60047.2023.00044 · Cited by 9

Unveiling Parkinson’s: Handwriting Symptoms with Explainable and Interpretable CNN Model

Zemmar, A., Bennour, A., Mekhaznia, T., Ghabban, F. M., & Al-Sarem, M. (2024) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001424400019 · Cited by 8

A machine learning approach for identifying anatomical biomarkers of early mild cognitive impairment

Ahmad, A. L., Sánchez‐Bornot, J. M., Sotero, R. C., Coyle, D., Idris, Z., & Faye, I. (2024) · PeerJ · doi:10.7717/peerj.18490 · Cited by 10

A deep learning based framework for solar panel segmentation and fault classification enhanced with explainable AI

Adib, A. U. R., Islam, M., Abid, M. S., & Ahshan, R. (2025) · Solar Energy · doi:10.1016/j.solener.2025.114058 · Cited by 8

OntoXAI: a semantic web rule language approach for explainable artificial intelligence

Sharma, S., & Jain, S. (2024) · Cluster Computing · doi:10.1007/s10586-024-04682-2 · Cited by 8

A Survey of Interpretability of Machine Learning in Accelerator-based High Energy Physics

Turvill, D., Barnby, L. S., Yuan, B., & Zahir, A. (2020) · IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT) · doi:10.1109/bdcat50828.2020.00025 · Cited by 9

QUANTIFYING EXPLAINABLE AI METHODS IN MEDICAL DIAGNOSIS: A STUDY IN SKIN CANCERPreprint

Sangwan, H. (2024) · medRxiv · doi:10.1101/2024.12.08.24318158 · Cited by 9

Machine Learning for Chronic Kidney Disease Detection from Planar and SPECT Scintigraphy: A Scoping Review

Vrbaški, D., Vesin, B., & Mangaroska, K. (2025) · Applied Sciences · doi:10.3390/app15126841 · Cited by 10

Smartphone-based prediction of dopaminergic deficit in prodromal and manifest Parkinson’s disease

Gunter, K. M., Groenewald, K., Aubourg, T., Lo, C., Welch, J., Razzaque, J., van Hillegondsberg, L., Nastasa, A., Ratti, P., Orso, B., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-02148-2 · Cited by 7

Action tremor features discovery for essential tremor and Parkinson's disease with explainable multilayer BiLSTM

Teo, Y. X., Lee, R. E., Nurzaman, S. G., Tan, C. P., & Chan, P. Y. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108957 · Cited by 10

Interpretable Models for the Potentially Harmful Content in Video Games Based on Game Rating Predictions

Zhipeng, F., & Gani, H. (2021) · Applied Artificial Intelligence · doi:10.1080/08839514.2021.2008148 · Cited by 7

From Fitness Landscapes to Explainable AI and Back

Thomson, S. L., Adair, J., Brownlee, A. E. I., & van den Berg, D. (2023) · Genetic and Evolutionary Computation Conference Companion (GECCO Companion) · doi:10.1145/3583133.3596395 · Cited by 7

Fully automatic categorical analysis of striatal subregions in dopamine transporter SPECT using a convolutional neural network

Buddenkotte, T., Lange, C., Klutmann, S., Apostolova, I. I., & Buchert, R. (2025) · Annals of Nuclear Medicine · doi:10.1007/s12149-025-02038-3 · Cited by 6

Artificial Intelligence in Action (2019-2024): A Review on Parkinson’s Disease Detection Using Non-Invasive Procedures

Patro, P., & Das, T. K. (2025) · IEEE Access · doi:10.1109/access.2025.3583572 · Cited by 6

Role of XAI in building a super smart society 5.0

Kiruthika, M., Moorthi, K., Devi, M. A., & Roseline, S. A. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95315-3.00013-9 · Cited by 7

Performance and Clinical Impact of Radiomics and 3D-CNN Models for the Diagnosis of Neurodegenerative Parkinsonian Syndromes on 18F-FDOPA PET

Le, T. K., Comte, V., Darcourt, J., Razzouk‐Cadet, M., Rollet, A.-C., Orlhac, F., & Humbert, O. (2024) · Clinical Nuclear Medicine · doi:10.1097/rlu.0000000000005392 · Cited by 6

A Review of Interpretable Deep Learning for Neurological Disease Classification

Katarya, R., Sharma, P., Soni, N., & Rath, P. (2022) · International Conference on Advanced Computing and Communication Systems (ICACCS) · doi:10.1109/icaccs54159.2022.9785321 · Cited by 6

Quantifying Inter-hemispheric Differences in Parkinson’s Disease Using Siamese Networks

Arco, J. E., Ortíz, A., Castillo-Barnés, D., Górriz, J. M., & Ramı́rez, J. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-06242-1_16 · Cited by 6

Counterfactual-Integrated Gradients: Counterfactual Feature Attribution for Medical Records

Duell, J., Seisenberger, M., & Fan, X. (2023) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm58861.2023.10385466 · Cited by 6

Leveraging Explainable AI Methods and Tools for Educational Data

Casalino, G., Castellano, G., Ducange, P., Fazzolari, M., Pecori, R., & Zaza, G. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-67351-1_7 · Cited by 6

XAI-based Data Visualization in Multimodal Medical DataPreprint

Sharma, S., Singh, M., Mcdaid, L. J., & Bhattacharyya, S. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.11.664302 · Cited by 7

A hybrid physics-constrained interpretable machine learning framework for stability assessment of natural gas hydrate-bearing submarine slopes

Dong, X., Liu, H., Wei, R., Liu, L., Jia, C., Sun, H. C., & Chu, W. (2025) · Engineering Analysis with Boundary Elements · doi:10.1016/j.enganabound.2025.106371 · Cited by 5

Towards a transparent and interpretable AI model for medical image classifications

Wen, B., Wu, Y., Daqqaq, T. S., & Chaddad, A. (2025) · Cognitive Neurodynamics · doi:10.1007/s11571-025-10343-w · Cited by 5

Deep learning interpretability in neuroimaging: A comprehensive survey and methodological recommendations

Rahman, M. M., Calhoun, V. D., & Plis, S. (2026) · Imaging Neuroscience · doi:10.1162/imag.a.1129 · Cited by 16

Artificial intelligence in diagnosis of neural disorders using biosignals and imaging

Subaşı, A., Subasi, T. N., & Özaltın, Ö. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-19073-5.00014-8 · Cited by 6

A machine learning approach towards assessing consistency and reproducibility: an application to graft survival across three kidney transplantation eras

Achilonu, O., Obaido, G., Ogbuokiri, B., Aruleba, K., Musenge, E., & Fabian, J. (2024) · Frontiers in Digital Health · doi:10.3389/fdgth.2024.1427845 · Cited by 5

Explainable AI for medical image analysis

Brás, C., Montenegro, H., Cai, L. Y., Corbetta, V., Huo, Y., Silva, W., Cardoso, J. S., Landman, B. A., & Išgum, I. (2025) · Elsevier eBooks · doi:10.1016/b978-0-44-323761-4.00028-6 · Cited by 6

Models versus Datasets: Reducing Bias through Building a Comprehensive IDS Benchmark

Ahmad, R., Alsmadi, I. M., Al-Hamdani, W. A., & Tawalbeh, L. (2021) · Future Internet · doi:10.3390/fi13120318 · Cited by 5

Cancer prediction using graph-based gene selection and explainable classifier

Rostami, M., & Oussalah, M. (2022) · Finnish Journal of eHealth and eWelfare · doi:10.23996/fjhw.111772 · Cited by 5

Exploring Longitudinal MRI-Based Deep Learning Analysis in Parkinson’s Patients - A Short Survey Focus on Handedness

Gu, Y., Wang, Z., Wang, Y., Gong, Y., & Li, C. (2024) · Cancer Insight · doi:10.58567/ci03010006 · Cited by 5

Detection of Parkinso’s disease using Convolutional Neural Networks and Data Augmentation with SPECT images

Dehghan, R., Naderan, M., & Alavi, S. E. (2022) · International Conference on Computer and Knowledge Engineering · doi:10.1109/iccke57176.2022.9960085 · Cited by 5

The Role of AI in the Management of Movement Disorders

Deik, A. (2025) · Seminars in Neurology · doi:10.1055/a-2596-5950 · Cited by 4

Parkinson’s disease early detection using hybrid attentive CNN-transformer model

Kumari, G. R. P., Kanth, M. R., & Kamal, M. V. (2025) · Neural Computing and Applications · doi:10.1007/s00521-025-11634-3 · Cited by 4

Explainable soft-voting classifier for heart disease prediction using SHAP and LIME

Walia, S., Walia, S., Bhardwaj, A., Arora, S., Aggarwal, S., & Siwach, P. (2026) · Discover Computing · doi:10.1007/s10791-026-09972-4 · Cited by 5

Exploring the Potential Imaging Biomarkers for Parkinson’s Disease Using Machine Learning Approach

Mushta, I., Kõks, S., Popov, A. O., & Lysenko, O. (2024) · Bioengineering · doi:10.3390/bioengineering12010011 · Cited by 4

Deep Convolutional Neural Networks on Multiclass Classification of Three-Dimensional Brain Images for Parkinson’s Disease Stage Prediction

Huang, G., Lai, W.-C., Chen, T., Hsu, C., Chen, H., Wu, Y., & Yeh, L.-R. (2025) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-025-01402-z · Cited by 4

ParkinsonNet: Classification Parkinson's Disease Model Based on Novel Deep Learning Structure

Al‐jumaili, S. (2023) · AURUM Journal of Engineering Systems and Architecture · doi:10.53600/ajesa.1382806 · Cited by 4

Designing of Medical Diagnostic System based on Blockchain and Artificial Intelligence Technologies

Nicolăescu, C.-G., Enescu, F. M., & Bizon, N. (2024) · International Conference on Electronics, Computers and Artificial Intelligence (ECAI) · doi:10.1109/ecai61503.2024.10607584 · Cited by 4

Model Interpretation Considering Both Time and Frequency Axes Given Time Series Data

Lee, W., Kim, G., Yu, J., & Kim, Y. (2022) · Applied Sciences · doi:10.3390/app122412807 · Cited by 4

Explainable AI Models for COVID-19 Diagnosis Using CT-Scan Images and Clinical Data

Boutorh, A., Rahim, H., & Bendoumia, Y. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-20837-9_15 · Cited by 4

Towards Explainability in mHealth Application for Mitigation of Forward Head Posture in Smartphone Users

Oyeleke, R. O., & Sorinolu, B. G. (2022) · IEEE International Conference on e-Health Networking, Applications and Services (Healthcom) · doi:10.1109/healthcom54947.2022.9982740 · Cited by 4

Explainable AI for the diagnosis of neurodegenerative diseases: Unveiling methods, opportunities, and challenges

Santhosh, A. J. S., Karthik, R., & Suganthi, K. (2025) · Computer Science Review · doi:10.1016/j.cosrev.2025.100821 · Cited by 5

Hybrid Capsule Networks for early Parkinson’s disease detection: Reclassifying SWEDD cases through SPECT image analysis

Boucherouite, J., Jbari, A., & Jilbab, A. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.109187 · Cited by 3

Prediction and interpretation of liquefaction occurrences using explainable machine learning models

Kumar, K., Samui, P., & Choudhary, S. S. (2025) · Sadhana · doi:10.1007/s12046-025-03005-4 · Cited by 3

Detection of Parkinson’s disease with neuroimaging modalities using machine learning and artificial intelligence: a systematic review

Sadabad, F. E., Honhar, P., Houshi, S., Nejati, S. F., Bagherieh, S., Brackett, A. L., Yazdanpanah, F., Çayır, S., Hosseini, M., Tagare, H. D., et al. (2026) · Neurological Sciences · doi:10.1007/s10072-025-08768-6 · Cited by 4

Application of Machine Learning for Image Processing in the Healthcare Sector

Priya, N., Satpathy, I., & Patnaik, B. C. M. (2023) · Advances in medical technologies and clinical practice book series · doi:10.4018/979-8-3693-0876-9.ch004 · Cited by 3

Machine Learning Approach for Analyzing 3-Year Outcomes of Patients with Brain Arteriovenous Malformation (AVM) after Stereotactic Radiosurgery (SRS)

Mallma, M. J. R., Vilca-Aguilar, M., Zuloaga-Rotta, L., Borja-Rosales, R., Salas-Ojeda, M., & Mauricio, D. (2023) · Diagnostics · doi:10.3390/diagnostics14010022 · Cited by 3

Classification models comparison from the user’s level of interpretability

Dobranská, L., Biceková, A., & Babič, F. (2023) · IEEE International Symposium on Computational Intelligence and Informatics (CINTI) · doi:10.1109/cinti59972.2023.10381999 · Cited by 3

Differentiating Pressure Ulcer Risk Levels through Interpretable Classification Models Based on Readily Measurable Indicators

Vera-Salmerón, E., Domínguez-Nogueira, C., Sáez, J. A., Romero‐Béjar, J. L., & Mota‐Romero, E. (2024) · Healthcare · doi:10.3390/healthcare12090913 · Cited by 3

Incorporating label uncertainty during the training of convolutional neural networks improves performance for the discrimination between certain and inconclusive cases in dopamine transporter SPECT

Kucerenko, A., Buddenkotte, T., Apostolova, I. I., Klutmann, S., Ledig, C., & Buchert, R. (2024) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-024-06988-0 · Cited by 3

Enhancing explainability in medical image classification and analyzing osteonecrosis X-ray images using shadow learner system

Wu, Y., Fong, S. J., & Liu, L. (2024) · Applied Intelligence · doi:10.1007/s10489-024-05916-x · Cited by 5

Enhancing breast cancer diagnosis: transfer learning on DenseNet with neural hashing for histopathology fine-grained image classification

Taheri, F., & Rahbar, K. (2025) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-025-03346-6 · Cited by 4

Explaining Machine Learning Predictions: A Case Study

Dutta, P., & Muppalaneni, N. B. (2022) · International Conference on Trends in Electrical, Electronics, and Computer Engineering (TEECCON) · doi:10.1109/teeccon54414.2022.9854821 · Cited by 3

Intelligent personalized diagnosis modeling in advanced medical system for Parkinson's disease using voice signals

Wen, P., Zhang, Y., & Wen, G. (2023) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2023351 · Cited by 3

A Deep Learning Model for Intrusion Detection with Imbalanced Dataset

Asry, C. E., Douzi, S., & Ouahidi, B. E. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-47672-3_26 · Cited by 3

Early knee osteoarthritis classification using distributed explainable convolutional neural network with local interpretable model-agnostic explanations

Kumar, M. G., Gumma, L. N., Neelam, S., Yaswanth, N., & Yedukondalu, J. (2024) · Engineering Research Express · doi:10.1088/2631-8695/ad9b6f · Cited by 3

A deep learning approach for classification of Parkinson’s disease using single photon emission computerized tomography

Ganotra, R., & Gupta, S. (2025) · Multimedia Tools and Applications · doi:10.1007/s11042-025-20661-0 · Cited by 3

Alzheimer’s And Parkinson’s Disease Classification Using Deep Learning Based On MRI: A Review

Suganya, A., & Aarthy, S. L. (2023) · International Journal of Communication Networks and Information Security (IJCNIS) · doi:10.17762/ijcnis.v14i1s.5588 · Cited by 3

An Ensemble of the Convolutional Neural Network Model with Fuzzy Fusion Rank Algorithm for the Identification of Parkinson’s Disease Using Magnetic Resonance Imaging Images

Mahendran, M., & Visalakshi, R. (2024) · Biomedical and Biotechnology Research Journal (BBRJ) · doi:10.4103/bbrj.bbrj_56_24 · Cited by 2

Data Mining Techniques for Predicting Energy Demand in Smart Grids: Optimizing Consumption and Production

Soni, P., & Tandan, G. (2025) · International Conference on Automation and Computation (AUTOCOM) · doi:10.1109/autocom64127.2025.10956845 · Cited by 2

Beyond just saying it’s false: explainable AI for multimodal misinformation detection

Roy, S., Bhanu, M., Priya, S., Chandra, J., & Dandapat, S. K. (2025) · Applied Intelligence · doi:10.1007/s10489-025-06656-2 · Cited by 2

A smart glove for Parkinson’s severity detection via low-frequency motion features and on-device machine learning

Parikh, P., Srivastava, V., Joshi, K. D., Shah, M. A., & Sharma, A. (2026) · Journal of low frequency noise, vibration and active control · doi:10.1177/14613484251414903 · Cited by 2

On the utility of ChatGPT in conducting a literature review on deep learning for dopamine transporter SPECT with [¹²³I]ioflupane

Boecker, H., Buchert, R., Buddenkotte, T., Apostolova, I. I., Opfer, R., Klutmann, S., & Braren, R. (2026) · Nuklearmedizin - NuclearMedicine · doi:10.1055/a-2890-8325 · Cited by 2

An Improved Approach for Parkinson’s Disease Classification Based on Convolutional Neural Network

Fourati, J., Othmani, M., & Ltifi, H. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-41774-0_10 · Cited by 2

Intrusion Detection System, a New Approach to R2L and U2R Attack Classification

Asry, C. E., Douzi, S., & Ouahidi, B. E. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-48465-0_39 · Cited by 2

Deciphering Parkinson's Disease Progression Using Deep Learning Techniques

Pandey, P. M., Mitra, S., Singh, S., & Singh, A. (2024) · SN Computer Science · doi:10.1007/s42979-024-03366-3 · Cited by 2

Enhancing Disease Diagnosis: Leveraging Machine Learning Algorithms for Healthcare Data Analysis

Ramteke, M., & Raut, S. (2024) · IETE Journal of Research · doi:10.1080/03772063.2024.2434572 · Cited by 2

Parkinson’s Disease Detection Using Machine Learning

Desai, S., Mehta, D., Dulera, V., & Chhikaniwala, H. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-19-2894-9_4 · Cited by 2

Exploring the Potential Imaging Biomarkers for Parkinson’s Disease Using Machine Learning Approach

Mushta, I., Kõks, S., Popov, A. O., & Lysenko, O. (2024) · Bioengineering · doi:10.3390/bioengineering12010011 · Cited by 4

Identification of latent biomarkers in brain imaging of Parkinson’s disease using explainable artificial intelligence

Noun, A., Soubra, R., & Chkeir, A. (2025) · HAL · doi:10.48340/ihc2025_pd012

Explainable AI Reveals Hidden Biomarkers in Parkinson's Brain Imaging

Noun, A., Soubra, R., & Chkeir, A. (2025) · International Conference on Bio-engineering for Smart Technologies (BioSMART) · doi:10.1109/biosmart66413.2025.11046020

An Explainable Approach for Early Parkinson Disease Detection Using Deep Learning

Aversano, L., Bernardi, M. L., Cimitile, M., Iammarino, M., Madau, A., & Verdone, C. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-39059-3_22 · Cited by 4

Explainable Parkinson's Disease Detection from MRI Scans Using Pretrained ResNet-18

Masud, N., Bassam, A. A., Sourav, M. T. I., & Sobhan, A. (2025) · IEEE International Conference on Quantum Photonics, Artificial Intelligence & Networking (QPAIN) · doi:10.1109/qpain66474.2025.11171852 · Cited by 1