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Explainable Artificial Intelligence in Alzheimer’s Disease Classification: A Systematic Review
Open accessPublished version of a preprint
Abstract
The unprecedented growth of computational capabilities in recent years has allowed Artificial Intelligence (AI) models to be developed for medical applications with remarkable results. However, a large number of Computer Aided Diagnosis (CAD) methods powered by AI have limited acceptance and adoption in the medical domain due to the typical blackbox nature of these AI models. Therefore, to facilitate the adoption of these AI models among the medical practitioners, the models' predictions must be explainable and interpretable. The emerging field of explainable AI (XAI) aims to justify the trustworthiness of these models' predictions. This work presents a systematic review of the literature reporting Alzheimer's disease (AD) detection using XAI that were communicated during the last decade. Research questions were carefully formulated to categorise AI models into different conceptual approaches (e.g., Post-hoc, Ante-hoc, Model-Agnostic, Model-Specific, Global, Local etc.) and frameworks (Local Interpretable Model-Agnostic Explanation or LIME, SHapley Additive exPlanations or SHAP, Gradient-weighted Class Activation Mapping or GradCAM, Layer-wise Relevance Propagation or LRP, etc.) of XAI. This categorisation provides broad coverage of the interpretation spectrum from intrinsic (e.g., Model-Specific, Ante-hoc models) to complex patterns (e.g., Model-Agnostic, Post-hoc models) and by taking local explanations to a global scope. Additionally, different forms of interpretations providing in-depth insight into the factors that support the clinical diagnosis of AD are also discussed. Finally, limitations, needs and open challenges of XAI research are outlined with possible prospects of their usage in AD detection.
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“This allows SHAP to provide unified, quantitative feature importance scores across fundamentally different data types”
From Multimodal neuroimaging and AI integration in cognitive disorders: advances, challenges, and future directions for precision medicine (2026) · cites this paper as Viswan et al., 2024
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Bogdanovic, B., Eftimov, T., & Simjanoska, M. (2022) · Scientific Reports · doi:10.1038/s41598-022-10202-2 · Cited by 77 · cited 6×
“The XAI-based AD papers in our study are broadly classified under three main categories: Authors in [125] provided local and global interpretation in the conversion of Dementia to MCI using the XGBoost algorithm.”
InfluentialShort-Term Prediction of COVID-19 Cases Using Machine Learning Models
Alyami, S., & Moni, M. A. (2021) · Applied Sciences · doi:10.3390/app11094266 · Cited by 71
Wang, D., Honnorat, N., Fox, P. T., Ritter, K., Eickhoff, S. B., Seshadri, S. S., & Habes, M. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.119929 · Cited by 70
Explainable Identification of Dementia From Transcripts Using Transformer Networks
Ilias, L., & Askounis, D. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3172479 · Cited by 70 · cited 5×
“Another article by Ilias et al. [131] has used speech The sunburst chart in Fig.”
InfluentialRecommender Systems: An Explainable AI Perspective
Vultureanu‐Albişi, A., & Bădică, C. (2021) · International Conference on Innovations in Intelligent Systems and Applications (INISTA) · doi:10.1109/inista52262.2021.9548125 · Cited by 70 · cited 1×
“XAI has been applied in drug discovery [106, 107], industrial applications [108, 109], gaming [110, 111], neurological disorders [112–114], neuroscience [115, 116] and recommender systems [117, 118].”
Nahian, M. J. A., Ghosh, T., Uddin, M. N., Islam, M. M., Mahmud, M., & Kaiser, M. S. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59277-6_25 · Cited by 70 · cited 1×
“…disorder detection and management [2, 33–44], supporting the detection and management of the COVID-19 pandemic [45–52], elderly monitoring and care [53–56], cyber security and trust management [57–62], various disease diagnosis [63–69], smart healthcare service delivery [70–72], text and social…”
Self-citationPrakash, N. B., Murugappan, M., Hemalakshmi, G. R., Jayalakshmi, M., & Mahmud, M. (2021) · Sustainable Cities and Society · doi:10.1016/j.scs.2021.103252 · Cited by 69
Self-citationBloch, L., Friedrich, C. M., & Initiative, F. T. A. D. N. (2021) · Alzheimer's Research & Therapy · doi:10.1186/s13195-021-00879-4 · Cited by 68 · cited 6×
“Bloch and Friedrich [123] propose a study that compares the classification using RF and XGBoost for volumetric measurements of MRI scans of control and dementia patients.”
InfluentialSecured Smart Healthcare System: Blockchain and Bayesian Inference Based Approach
Farhin, F., Kaiser, M. S., & Mahmud, M. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-33-4673-4_36 · Cited by 66 · cited 1×
“…monitoring and care [53–56], cyber security and trust management [57–62], various disease diagnosis [63–69], smart healthcare service delivery [70–72], text and social media mining [73–76], personalised learning [77–80], earthquake detection [81, 82], etc. Part of these methods has…”
Self-citationExplanation-Driven HCI Model to Examine the Mini-Mental State for Alzheimer’s Disease
Gaur, L., Bhandari, M., Shikhar, B. S., Jhanjhi, N. Z., Shorfuzzaman, M., & Masud, M. (2022) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3527174 · Cited by 65 · cited 3×
“There have been several noteworthy applications of XAI in AD classification too [120–134] However, an exclusive systematic review article on XAI for AD classification that points to various XAI frameworks and blackbox models used inside these frameworks is yet to be proposed by the research…”
DIG: A Turnkey Library for Diving into Graph Deep Learning ResearchPreprint
Liu, M., Luo, Y., Wang, L., Xie, Y., Yuan, H., Gui, S., Yu, H., Zhao, X., Zhang, J., Liu, Y., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2103.12608 · Cited by 62
A Novel AI-Based System for Detection and Severity Prediction of Dementia Using MRI
Jain, V., Nankar, O., Jerrish, D. J., Gite, S., Patil, S., & Kotecha, K. V. (2021) · IEEE Access · doi:10.1109/access.2021.3127394 · Cited by 62 · cited 2×
“Jain et al. [160] construct a heatmap emphasising the characteristics discovered from the input MRI scan for each layer of the CNN model using GradCAM.”
Mediating Community-AI Interaction through Situated Explanation
Kou, Y., & Gui, X. (2020) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3415173 · Cited by 61 · cited 1×
“determine what establishes a good explanation without vali-38 dating with a professional from the medical domain [175].”
Fabietti, M. I., Mahmud, M., Lotfi, A., Averna, A., Guggenmos, D. J., Nudo, R. J., & Chiappalone, M. (2020) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9207320 · Cited by 59
Self-citationGonzález‐Domínguez, R., Castellano‐Escuder, P., Lefèvre‐Arbogast, S., Low, D. Y., Preez, A. D., Ruigrok, S. R., Lee, H.-A., Helmer, C., Pallàs, M., Urpí-Sardà, M., et al. (2022) · Alzheimer s Research & Therapy · doi:10.1186/s13195-021-00948-8 · Cited by 58
Detection of Breast Cancer from Five-View Thermal Images Using Convolutional Neural Networks
Mammoottil, M. J., Kulangara, L. J., Cherian, A., Mohandas, P., Hasikin, K., & Mahmud, M. (2022) · Journal of Healthcare Engineering · doi:10.1155/2022/4295221 · Cited by 57
Self-citationAutomatic Lung Health Screening Using Respiratory Sounds
Mukherjee, H., Sreerama, P., Dhar, A., Obaidullah, S. M., Roy, K., Mahmud, M., & Santosh, K. (2021) · Journal of medical systems · doi:10.1007/s10916-020-01681-9 · Cited by 57
Self-citationExplainable Multimodal Machine Learning for Engagement Analysis by Continuous Performance Test
Rahman, M. A., Brown, D. J., Shopland, N., Burton, A. M., & Mahmud, M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-05039-8_28 · Cited by 56
Self-citationMachine Learning in Fighting Pandemics: A COVID-19 Case Study
Mahmud, M., & Kaiser, M. S. (2020) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-15-9682-7_9 · Cited by 54
Self-citationExplainable AI for Cheating Detection and Churn Prediction in Online Games
Tao, J., Xiong, Y., Zhao, S., Wu, R., Shen, X., Lyu, T., Fan, C., Hu, Z., Zhao, S., & Pan, G. (2022) · IEEE Transactions on Games · doi:10.1109/tg.2022.3173399 · Cited by 53 · cited 1×
“XAI has been applied in drug discovery [106, 107], industrial applications [108, 109], gaming [110, 111], neurological disorders [112–114], neuroscience [115, 116] and recommender systems [117, 118].”
Oh, K., Yoon, J. S., & Suk, H. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3197845 · Cited by 52
Effect of Corpora on Classification of Fake News using Naive Bayes Classifier
Adiba, F. I., Islam, T., Kaiser, M., Mahmud, M., & Rahman, M. A. (2020) · International Journal of Automation, Artificial Intelligence and Machine Learning · doi:10.61797/ijaaiml.v1i1.45 · Cited by 50 · cited 1×
“…security and trust management [57–62], various disease diagnosis [63–69], smart healthcare service delivery [70–72], text and social media mining [73–76], personalised learning [77–80], earthquake detection [81, 82], etc. Part of these methods has significantly boosted clinical diagnosis of AD…”
Self-citationForecasting the Risk of Type II Diabetes using Reinforcement Learning
Zohora, M. F., Tania, M. H., Kaiser, M. S., & Mahmud, M. (2020) · International Conference on Informatics, Electronics & Vision (ICIEV) · doi:10.1109/icievicivpr48672.2020.9306653 · Cited by 50 · cited 1×
“…of the COVID-19 pandemic [45–52], elderly monitoring and care [53–56], cyber security and trust management [57–62], various disease diagnosis [63–69], smart healthcare service delivery [70–72], text and social media mining [73–76], personalised learning [77–80], earthquake detection [81,…”
Self-citationRuengchaijatuporn, N., Chatnuntawech, I., Teerapittayanon, S., Sriswasdi, S., Itthipuripat, S., Hemrungrojn, S., Bunyabukkana, P., Petchlorlian, A., Chunamchai, S., Chotibut, T., et al. (2022) · Alzheimer s Research & Therapy · doi:10.1186/s13195-022-01043-2 · Cited by 49 · cited 5×
“Ruengchaijatuporn et al. [124] use images of bedside tasks like clock drawing tests, cube-copying and tail-making tests to classify between HC and AD patients in a deep neural network.”
InfluentialAlzheimer's Disease Analysis using Explainable Artificial Intelligence (XAI)
Sudar, K., Nagaraj, P., Nithisaa, S., Aishwarya, R., Aakash, M., & Lakshmi, S. I. (2022) · 2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS) · doi:10.1109/icscds53736.2022.9760858 · Cited by 47
fASSERT: A Fuzzy Assistive System for Children with Autism Using Internet of Things
Sumi, A. I., Zohora, M. F., Mahjabeen, M., Faria, T. J., Mahmud, M., & Kaiser, M. S. (2018) · BI · doi:10.1007/978-3-030-05587-5_38 · Cited by 47 · cited 1×
“…anomaly detection [7–9], biosignal and image analysis [10–21], neurodevelopmental disorder assessment and classification focusing on autism [22–32], neurological disorder detection and management [2, 33–44], supporting the detection and management of the COVID-19 pandemic [45–52], elderly…”
Self-citationSanti, L. A. D., Pasini, E., Santarelli, M. F., Genovesi, D., & Positano, V. (2022) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-022-00719-3 · Cited by 47
Explainable AI in Industry: Practical Challenges and Lessons Learned
Gade, K., Geyik, S., Kenthapadi, K., Mithal, V., & Taly, A. (2020) · Companion Proceedings of the Web Conference 2020 · doi:10.1145/3366424.3383110 · Cited by 46
Arrotta, L., Civitarese, G., & Bettini, C. (2022) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3517224 · Cited by 45
A novel explainable neural network for Alzheimer's disease diagnosis
Yu, L., Xiang, W., Fang, J., Chen, Y.-P. P., & Zhu, R. (2022) · Pattern Recognition · doi:10.1016/j.patcog.2022.108876 · Cited by 44
Towards a data-driven adaptive anomaly detection system for human activity
Yahaya, S., Lotfi, A., & Mahmud, M. (2021) · Pattern Recognition Letters · doi:10.1016/j.patrec.2021.02.006 · Cited by 44
Self-citationDeepa, B., Murugappan, M., Sumithra, M., Mahmud, M., & Al-Rakhami, M. S. (2022) · IEEE Access · doi:10.1109/access.2021.3100549 · Cited by 44
Self-citationGarcía‐Gutiérrez, F., Álvarez, J. D., Matías‐Guiu, J. A., Pytel, V., Matías‐Guiu, J., Cabrera‐Martín, M. N., & Ayala, J. L. (2022) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-022-02630-z · Cited by 44 · cited 1×
“In another study, García-Gutierrez et al. [162] proposed a diagnostic tool that uses a DT that provides a simple and unambiguous set of decision rules to provide capabilities to clinicians to give insights into the pathophysiology of AD and behavioural Fronto Temporal Dementia (bvFTD).”
Lalotra, G. S., Kumar, V., Bhatt, A., Chen, T., & Mahmud, M. (2022) · Security and Communication Networks · doi:10.1155/2022/9149164 · Cited by 44
Self-citationNahiduzzaman, M., Tasnim, M., Newaz, N. T., Kaiser, M., & Mahmud, M. (2020) · BI · doi:10.1007/978-3-030-59277-6_19 · Cited by 41
Self-citationHernández, M., Ramon-Julvez, U., & Ferraz, F. M. (2022) · PLoS ONE · doi:10.1371/journal.pone.0264695 · Cited by 40
Paul, A., Basu, A., Mahmud, M., Kaiser, M., & Sarkar, R. (2022) · Neural computing & applications (Print) · doi:10.1007/s00521-021-06737-6 · Cited by 40
Self-citationRough Sets in COVID-19 to Predict Symptomatic Cases
Bhapkar, H., Mahalle, P., Shinde, G., & Mahmud, M. (2020) · COVID-19: Prediction, Decision-Making, and its Impacts · doi:10.1007/978-981-15-9682-7_7 · Cited by 40
Self-citationAn XAI Based Autism Detection: The Context Behind the Detection
Biswas, M., Kaiser, M., Mahmud, M., Mamun, S. A., Hossain, M. S., & Rahman, M. A. (2021) · BI · doi:10.1007/978-3-030-86993-9_40 · Cited by 40
Self-citationChun, M.-Y., Park, C. J., Kim, J.-H., Jeong, J., Jang, H., Kim, K., & Seo, S. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.898940 · Cited by 39 · cited 7×
“[126].”
InfluentialExploring Alzheimer's Disease Prediction with XAI in various Neural Network Models
Shad, H. A., Rahman, Q. A., Asad, N. B., Bakshi, A., Mursalin, S. M. F., Reza, M., & Parvez, M. (2021) · IEEE Region 10 Conference · doi:10.1109/tencon54134.2021.9707468 · Cited by 37 · cited 1×
“The predictions about the likely outcome of the generated data are visualised using LIME by Shad et al. [128] and Sidulova et al. [157].”
Jesmin, S., Kaiser, M. S., & Mahmud, M. (2020) · IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT) · doi:10.1109/wiiat50758.2020.00130 · Cited by 37
Self-citationArtificial and Internet of Healthcare Things Based Alzheimer Care During COVID 19
Jesmin, S., Kaiser, M., & Mahmud, M. (2020) · BI · doi:10.1007/978-3-030-59277-6_24 · Cited by 36 · cited 1×
“…on autism [22–32], neurological disorder detection and management [2, 33–44], supporting the detection and management of the COVID-19 pandemic [45–52], elderly monitoring and care [53–56], cyber security and trust management [57–62], various disease diagnosis [63–69], smart healthcare service…”
Self-citationEstimating explainable Alzheimer’s disease likelihood map via clinically-guided prototype learning
Mulyadi, A. W., Jung, W., Oh, K., Yoon, J. S., Lee, K. H., & Suk, H. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120073 · Cited by 36
Bloch, L., Friedrich, C. M., & Initiative, A. D. N. (2022) · SN Computer Science · doi:10.1007/s42979-022-01371-y · Cited by 34 · cited 4×
“SHAP is used by Salih et al. [159] and Bloch et al. [161] to determine the order of informative predictors in test data.”
InfluentialShaffi, N., Hajamohideen, F., Mahmud, M., Abdesselam, A., Karthikeyan, S., & Sariri, A. A. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-15037-1_23 · Cited by 34
Self-citationMahmud, M., Kaiser, M., Rahman, M. A., Wadhera, T., Brown, D. J., Shopland, N., Burton, A., Hughes-Roberts, T., Mamun, S. A., Ieracitano, C., et al. (2022) · Interacción · doi:10.1007/978-3-031-05039-8_26 · Cited by 33
Self-citationAnomaly Detection in Electroencephalography Signal Using Deep Learning Model
Tahura, S., Samiul, S. M. H., Kaiser, M. S., & Mahmud, M. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-33-4673-4_18 · Cited by 33
Self-citationGalazzo, I. B., Cruciani, F., Brusini, L., Salih, A. M., Radeva, P. I., Storti, S. F., & Menegaz, G. (2022) · IEEE Signal Processing Magazine · doi:10.1109/msp.2021.3126573 · Cited by 32 · cited 1×
“XAI has been applied in drug discovery [106, 107], industrial applications [108, 109], gaming [110, 111], neurological disorders [112–114], neuroscience [115, 116] and recommender systems [117, 118].”
Rahman, M. A., Brown, D. J., Mahmud, M., Harris, M. C., Shopland, N., Heym, N., Sumich, A., Turabee, Z., Standen, B., Downes, D., et al. (2023) · Brain Informatics · doi:10.1186/s40708-023-00193-9 · Cited by 31 · cited 1×
“…various disease diagnosis [63–69], smart healthcare service delivery [70–72], text and social media mining [73–76], personalised learning [77–80], earthquake detection [81, 82], etc. Part of these methods has significantly boosted clinical diagnosis of AD in an incredibly accurate, fast,…”
Self-citationLai, Y., Lin, X., Lin, C., Lin, X., Chen, Z., & Zhang, L. (2022) · Frontiers in Pharmacology · doi:10.3389/fphar.2022.975774 · Cited by 31 · cited 3×
“It impedes testing of the model on an extensive dataset which is crucial in determining the actual robustness [131, 155, 156, 179].”
Ensemble Classifiers for a 4-Way Classification of Alzheimer’s Disease
Shaffi, N., Hajamohideen, F., Abdesselam, A., Mahmud, M., & Karthikeyan, S. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-24801-6_16 · Cited by 31
Self-citationTasnim, N., Mamun, S. A., Islam, M. S., Kaiser, M. S., & Mahmud, M. (2023) · Applied Sciences · doi:10.3390/app13106138 · Cited by 30 · cited 1×
“…of the COVID-19 pandemic [45–52], elderly monitoring and care [53–56], cyber security and trust management [57–62], various disease diagnosis [63–69], smart healthcare service delivery [70–72], text and social media mining [73–76], personalised learning [77–80], earthquake detection [81, 82],…”
Self-citationFabietti, M. I., Mahmud, M., Lotfi, A., Averna, A., Guggenmos, D. J., Nudo, R. J., & Chiappalone, M. (2020) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci47803.2020.9308165 · Cited by 30
Self-citationAkhund, T. M. N. U., Mahi, M., Tanvir, A., Mahmud, M., & Kaiser, M. (2018) · BI · doi:10.1007/978-3-030-05587-5_39 · Cited by 29 · cited 1×
“…[10–21], neurodevelopmental disorder assessment and classification focusing on autism [22–32], neurological disorder detection and management [2, 33–44], supporting the detection and management of the COVID-19 pandemic [45–52], elderly monitoring and care [53–56], cyber security and trust…”
Self-citationWadhera, T., & Mahmud, M. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3232550 · Cited by 29
Self-citationKim, M., Kim, J., Qu, J., Huang, H., Long, Q., Sohn, K.-A., Kim, D., & Shen, L. (2021) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm52615.2021.9669504 · Cited by 28 · cited 3×
“The study proposed by Kim et al. [129] uses an interpretable Graph Neural Network (GNN) for classifying AD and MCI.”
InfluentialA Flexible Keyphrase Extraction Technique for Academic Literature
Rabby, G., Azad, S., Mahmud, M., Zamli, K. Z., & Rahman, M. M. (2018) · International Conference on Computer Science and Computational Intelligence · doi:10.1016/j.procs.2018.08.208 · Cited by 28
Self-citationFabietti, M. I., Mahmud, M., & Lotfi, A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59277-6_14 · Cited by 28
Self-citationRahman, S., Sharma, T., & Mahmud, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59277-6_22 · Cited by 26
Self-citationAttack Detection in Internet of Things using Software Defined Network and Fuzzy Neural Network
Farhin, F., Sultana, I., Islam, N., Kaiser, M. S., Rahman, M. S., & Mahmud, M. (2020) · International Conference on Informatics, Electronics and Vision · doi:10.1109/icievicivpr48672.2020.9306666 · Cited by 25
Self-citationGetting Playful with Explainable AI: Games with a Purpose to Improve Human Understanding of AI
Fulton, L. B., Wang, Q., Hammer, J., Lee, J. Y., Yuan, Z., & Perer, A. (2020) · CHI Extended Abstracts · doi:10.1145/3334480.3382831 · Cited by 25 · cited 1×
“XAI has been applied in drug discovery [106, 107], industrial applications [108, 109], gaming [110, 111], neurological disorders [112–114], neuroscience [115, 116] and recommender systems [117, 118].”
A dominant set-informed interpretable fuzzy system for automated diagnosis of dementia
Chen, T., Su, P., Shen, Y., Chen, L., Mahmud, M., Zhao, Y., & Antoniou, G. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.867664 · Cited by 24
Self-citationAhuja, N. J., Thapliyal, M., Bisht, A., Stephan, T., Kannan, R., Al-Rakhami, M. S., & Mahmud, M. (2022) · IEEE Access · doi:10.1109/access.2021.3115409 · Cited by 24 · cited 1×
“…[57–62], various disease diagnosis [63–69], smart healthcare service delivery [70–72], text and social media mining [73–76], personalised learning [77–80], earthquake detection [81, 82], etc. Part of these methods has significantly boosted clinical diagnosis of AD in an incredibly accurate, fast,…”
Self-citationIndoor Navigation Support System for Patients with Neurodegenerative Diseases
Biswas, M., Rahman, A. F. M. M., Kaiser, M., Mamun, S., Mizan, K., Islam, M. S., & Mahmud, M. (2021) · BI · doi:10.1007/978-3-030-86993-9_37 · Cited by 24
Self-citationComprehensive Analysis of Nature-Inspired Algorithms for Parkinson’s Disease Diagnosis
Shafiq, S., Ahmed, S., Kaiser, M. S., Mahmud, M., Hossain, M. S., & Andersson, K. (2022) · IEEE Access · doi:10.1109/access.2022.3232292 · Cited by 24
Self-citationMellem, M. S., Kollada, M., Tiller, J., & Lauritzen, T. Z. (2021) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-021-01510-0 · Cited by 23 · cited 1×
“XAI has been applied in drug discovery [106, 107], industrial applications [108, 109], gaming [110, 111], neurological disorders [112–114], neuroscience [115, 116] and recommender systems [117, 118].”
Towards Secured Service Provisioning for the Internet of Healthcare Things
Farhin, F., Kaiser, M. S., & Mahmud, M. (2020) · Advanced Industrial Conference on Telecommunications · doi:10.1109/aict50176.2020.9368580 · Cited by 23 · cited 1×
“…supporting the detection and management of the COVID-19 pandemic [45–52], elderly monitoring and care [53–56], cyber security and trust management [57–62], various disease diagnosis [63–69], smart healthcare service delivery [70–72], text and social media mining [73–76], personalised learning…”
Self-citationA machine learning pipeline to classify foetal heart rate deceleration with optimal feature set
Das, S., Obaidullah, S. M., Mahmud, M., Kaiser, M. S., Roy, K., Saha, C. K., & Goswami, K. (2023) · Scientific Reports · doi:10.1038/s41598-023-27707-z · Cited by 22
Self-citationNeurorobots as a Means Toward Neuroethology and Explainable AI
Chen, K., Hwu, T. J., Kashyap, H. J., Krichmar, J. L., Stewart, K. M., Xing, J., & Zou, X. (2020) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2020.570308 · Cited by 22 · cited 1×
“XAI has been applied in drug discovery [106, 107], industrial applications [108, 109], gaming [110, 111], neurological disorders [112–114], neuroscience [115, 116] and recommender systems [117, 118].”
SMILE: systems metabolomics using interpretable learning and evolution
Sha, C., Čuperlović‐Culf, M., & Hu, T. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04209-1 · Cited by 22 · cited 3×
“Authors in [153] have used a metabolomic dataset to identify the key metabolites and their interaction associated with AD.”
InfluentialBordin, V., Coluzzi, D., Rivolta, M., & Baselli, G. (2022) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society · doi:10.1109/embc48229.2022.9871306 · Cited by 21 · cited 4×
“Another closely related issue with the dataset is the issue of class imbalance [123, 165].”
InfluentialAkter, T., Ali, M., Satu, M. S., Khan, M. I., & Mahmud, M. (2021) · BI · doi:10.1007/978-3-030-86993-9_36 · Cited by 21
Self-citationWanner, J., Herm, L.-V., Heinrich, K., & Janiesch, C. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-85447-8_22 · Cited by 21 · cited 1×
“There is often a trade-off between model accuracy and associated explainability [104].”
Fabietti, M. I., Mahmud, M., Lotfi, A., Leparulo, A., Fontana, R., Vassanelli, S., & Fasolato, C. (2023) · IEEE transactions on neural systems and rehabilitation engineering · doi:10.1109/tnsre.2023.3288835 · Cited by 20 · cited 1×
“…[10–21], neurodevelopmental disorder assessment and classification focusing on autism [22–32], neurological disorder detection and management [2, 33–44], supporting the detection and management of the COVID-19 pandemic [45–52], elderly monitoring and care [53–56], cyber security and trust…”
Self-citationSalih, A. M., Galazzo, I. B., Cruciani, F., Brusini, L., & Radeva, P. I. (2022) · Proceedings - International Conference on Image Processing · doi:10.1109/icip46576.2022.9897253 · Cited by 20 · cited 3×
“In another study, Salih et al. [159] try to develop a proxy that will check for stability in the explanation by choosing the proper XAI method, classifier, and available data.”
Motin, M. A., Mahmud, M., & Brown, D. J. (2022) · IEEE International Conference on Application of Information and Communication Technologies (AICT) · doi:10.1109/aict55583.2022.10013589 · Cited by 20
Self-citationWadhera, T., & Mahmud, M. (2022) · IEEE International Joint Conference on Neural Network · doi:10.1109/ijcnn55064.2022.9892799 · Cited by 17
Self-citationKorda, A., Andreou, C., Rogg, H. V., Avram, M., Ruef, A., Davatzikos, C., Koutsouleris, N., & Borgwardt, S. (2022) · Translational Psychiatry · doi:10.1038/s41398-022-02242-z · Cited by 16
A Deep Concatenated Convolutional Neural Network-Based Method to Classify Autism
Wadhera, T., Mahmud, M., & Brown, D. J. (2022) · International Conference on Neural Information Processing · doi:10.1007/978-981-99-1648-1_37 · Cited by 15 · cited 1×
“…anomaly detection [7–9], biosignal and image analysis [10–21], neurodevelopmental disorder assessment and classification focusing on autism [22–32], neurological disorder detection and management [2, 33–44], supporting the detection and management of the COVID-19 pandemic [45–52], elderly…”
Self-citationFabietti, M. I., Mahmud, M., Lotfi, A., Averna, A., Guggenmos, D., Nudo, R., & Chiappalone, M. (2020) · Advanced Industrial Conference on Telecommunications · doi:10.1109/aict50176.2020.9368638 · Cited by 14
Self-citationContactless Fall Detection for the Elderly
Nahian, M. J. A., Raju, M. H., Tasnim, Z., Mahmud, M., Ahad, M., & Kaiser, M. (2021) · Intelligent systems reference library · doi:10.1007/978-3-030-68590-4_8 · Cited by 13 · cited 1×
“…detection and management [2, 33–44], supporting the detection and management of the COVID-19 pandemic [45–52], elderly monitoring and care [53–56], cyber security and trust management [57–62], various disease diagnosis [63–69], smart healthcare service delivery [70–72], text and social media…”
Self-citationWadhera, T., & Mahmud, M. (2022) · BI · doi:10.1007/978-3-031-15037-1_5 · Cited by 12
Self-citationiConDet: An Intelligent Portable Healthcare App for the Detection of Conjunctivitis
Mukherjee, P., Bhattacharyya, I., Mullick, M., Kumar, R., Roy, N., & Mahmud, M. (2021) · Analogical and Inductive Inference · doi:10.1007/978-3-030-82269-9_3 · Cited by 12
Self-citationTrust IoHT: A Trust Management Model for Internet of Healthcare Things
Esha, N. H., Tasmim, M., Huq, S., Mahmud, M., & Kaiser, M. (2020) · doi:10.1007/978-981-15-7561-7_3 · Cited by 12
Self-citationXu, X., & Yan, X.-Y. (2022) · IEEE International Conference on Advances in Electrical Engineering and Computer Applications (AEECA) · doi:10.1109/aeeca55500.2022.9918895 · Cited by 12 · cited 3×
“The study of Xu et al. [152] involves deep learning models that include Random Seeds and Nested Cross-validation, SVM-SMOTE, and RF for a three-way AD classification.”
InfluentialInterpretability of deep neural networks used for the diagnosis of Alzheimer's disease
Pohl, T., Jakab, M., & Benešová, W. (2021) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22657 · Cited by 11 · cited 5×
“From Table 7, the articles [101, 121, 130, 158, 159], and [132] propose classifiers of deep neural networks for prediction and classification between HC, MCI and AD.”
InfluentialAn Attention-Based Mood Controlling Framework for Social Media Users
Ghosh, T., Banna, M. H. A., Angona, T. M., Nahian, M. J. A., Uddin, M. N., Kaiser, M., & Mahmud, M. (2021) · BI · doi:10.1007/978-3-030-86993-9_23 · Cited by 9
Self-citationTowards the Development of an Adaptive System for Detecting Anomaly in Human Activities
Yahaya, S., Lotfi, A., & Mahmud, M. (2020) · IEEE Symposium Series on Computational Intelligence · doi:10.1109/ssci47803.2020.9308415 · Cited by 7
Self-citationMamun, S. A., Kaiser, M., & Mahmud, M. (2021) · BI · doi:10.1007/978-3-030-86993-9_15 · Cited by 7
Self-citationDetection of Healthy and Unhealthy Brain States from Local Field Potentials Using Machine Learning
Fabietti, M. I., Mahmud, M., Lotfi, A., Leparulo, A., Fontana, R., Vassanelli, S., & Fassolato, C. (2022) · BI · doi:10.1007/978-3-031-15037-1_3 · Cited by 6
Self-citationSutton, S., Mahmud, M., Singh, R. P., & Yovera, L. (2022) · Analogical and Inductive Inference · doi:10.1007/978-3-031-24801-6_17 · Cited by 3 · cited 1×
“…deep learning (DL) algorithms have contributed to diverse application domains including: anomaly detection [7–9], biosignal and image analysis [10–21], neurodevelopmental disorder assessment and classification focusing on autism [22–32], neurological disorder detection and management [2, 33–44],…”
Self-citation(2017) · doi:10.9728/dcs.2024.25.7.1847 · Cited by 2 · cited 2×
“Some popular model-agnostic approaches are LIME [100] and SHapley Additive exPla-nations (SHAP) [140].”
Research works printed but not linked: 20
- McDade EM. Alzheimer Disease. CONTINUUM: Lifelong Learning in Neurology. 2022;28(3):648–75. · DOI
- Fabietti M, Mahmud M, Lotfi A. Artefact Detection in Chronically Recorded Local Field Potentials: An Explainable Machine Learning-based Approach. In: Proc. IJCNN 2022; 1–7. · DOI
- Singh R, Mahmud M, Yovera L. Classification of First Trimester Ultrasound Images Using Deep Convolutional Neural Network. In: Proc AII 2021;92–105. · DOI
- Ahmed S, et al. Toward Machine Learning-Based Psychological Assessment of Autism Spectrum Disorders in School and Community. In: Proc TEHI 2022;139–49. · DOI
- Wadhera T, Mahmud M. Brain Networks in Autism Spectrum Disorder, Epilepsy and Their Relationship: A Machine Learning Approach. In: Artificial Intelligence in Healthcare; 2022;125–42. · DOI
- Noor MBT, Zenia NZ, Kaiser MS, Mamun SA, Mahmud M. Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer’s disease. Parkinson’s disease and schizophrenia Brain informatics. 2020;7(1):1–21.
- Shaffi N, Mahmud M, Hajamohideen F, Subramanian K, Kaiser MS. Machine Learning and Deep Learning Methods for the Detection of Schizophrenia using Magnetic Resonance Images and EEG Signals: An Overview of the Recent Advancements. In: Proc. ICTCS 2022;1-18. · DOI
- Banerjee JS, Chakraborty A, Mahmud M, Kar U, Lahby M, Saha G. Explainable Artificial Intelligence (XAI) Based Analysis of Stress Among Tech Workers Amidst COVID-19 Pandemic. In: Advanced AI and Internet of Health Things for Combating Pandemics 2023;151–74. · DOI
- Rahman MA, Brown DJ, Shopland N, Harris MC, Turabee ZB, Heym N, et al. Towards machine learning driven self-guided virtual reality exposure therapy based on arousal state detection from multimodal data. In: International Conference on Brain Informatics. Springer; 2022;195–209. · DOI
- Nazar M, Alam MM, Yafi E, Su’ud MM. A systematic review of human-computer interaction and explainable artificial intelligence in healthcare with artificial intelligence techniques. IEEE Access. 2021;9:153316–48. · DOI · Paper page
- Kumar D, Mehta MA. 3. In: An Overview of Explainable AI Methods, Forms and Frameworks. Cham: Springer International Publishing; 2023;43–59. Available from: https://doi.org/10.1007/978-3-031-12807-3_3. · DOI
- Pawar U, O’Shea D, Rea S, O’Reilly R. Explainable ai in healthcare. In: 2020 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA). IEEE 2020;1–2 · DOI
- Jung YJ, Han SH, Choi HJ. Explaining CNN and RNN using selective layer-wise relevance propagation. IEEE Access. 2021;9:18670–81. · DOI
- Ravi M, Negi A, Comparative Chitnis S. A Review of Expert Systems, Recommender Systems, and Explainable AI. In,. IEEE 7th International conference for Convergence in Technology (I2CT). IEEE. 2022;1–8. · DOI
- Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision; 2017;618;26. · DOI · Paper page
- Anders CJ, Neumann D, Samek W, Müller KR, Lapuschkin S. Software for Dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAy. CoRR. 2021;abs/2106.13200.
- Sidulova M, Nehme N, Towards Park CH. Analysis Explainable Image, for Alzheimer’s Disease and Mild Cognitive Impairment Diagnosis. In,. IEEE Applied Imagery Pattern Recognition Workshop (AIPR). IEEE. 2021;2021:1–6. · DOI
- Selvaraju R, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. Grad-CAM: visual explanations from deep networks via gradient-based localization. 2016. arXiv preprint http://arxiv.org/abs/1610.02391. 2016. · DOI · Paper page
- Ying Z, Bourgeois D, You J, Zitnik M, Leskovec J. Gnnexplainer: Generating explanations for graph neural networks. Advances in neural information processing systems. 2019;32.
- Bhandari M, Shahi TB, Siku B, Neupane A. Explanatory classification of CXR images into COVID-19, Pneumonia and Tuberculosis using deep learning and XAI. Computers in Biology and Medicine. 2022;150. · DOI
Cited by
Sorted by citations
Vimbi, V., Shaffi, N., & Mahmud, M. (2024) · Brain Informatics · doi:10.1186/s40708-024-00222-1 · Cited by 433
Self-citationKale, M. B., Wankhede, N. L., Pawar, R. S., Ballal, S., Kumawat, R., Goswami, M., Khalid, M., Taksande, B. G., Upaganlawar, A. B., Umekar, M. J., et al. (2024) · Ageing Research Reviews · doi:10.1016/j.arr.2024.102497 · Cited by 208
Hassan, S. U., Abdulkadir, S. J., Zahid, M. S. M., & Al-Selwi, S. M. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109569 · Cited by 123
Khan, F. S., Mazhar, S. S., Mazhar, K., AlSaleh, D., & Mazhar, A. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11215-9 · Cited by 128
Alsabah, M. Q., Naser, M. A., Albahri, A. S., Albahri, A. S., Alamoodi, A. H., Abdulhussain, S. H., & Alzubaidi, L. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11342-3 · Cited by 92
Metadata
- Authors
- Viswan, V., Shaffi, N., Mahmud, M., Subramanian, K., & Hajamohideen, F.
- Affiliations
- University of Technology and Applied Sciences-Suhar (OM)
- Nottingham Trent University (GB)
- Corresponding author
- Mahmud, Mufti
- Venue
- Cognitive Computation
- Published
- 2023-11-13
- Volume
- 16
- Issue
- 1
- Pages
- 1-44
- ISSN
- 1866-9956 (ISSN-L)
- 1866-9964
- OpenAlex
- W4388634755
- Cited by
- 195
- References
- 184
- Citations by year
- 2026: 63 · 2025: 80 · 2024: 49 · 2023: 3
- FWCI
- 4.08
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Cognitive Computation (publishedVersion, cc-by)
- Research Square (acceptedVersion, cc-by)
- Funders
- Ministry of Higher Education, Government of Oman (MoHERI/BFP/UoTAS/01/2021)
- Nottingham Trent University (Strategic Research Theme Award, 2022)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- explainable AI, Alzheimer's disease detection, computer-aided diagnosis, model interpretability, post-hoc explanation, model-agnostic explanation, local and global explanations, lime, SHAP, Grad-CAM, layer-wise relevance propagation, diagnosis
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