{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T15:26:49Z","timestamp":1793374009221,"version":"4.5.0"},"reference-count":138,"publisher":"Public Library of Science (PLoS)","issue":"6","license":[{"start":{"date-parts":[[2025,6,4]],"date-time":"2025-06-04T00:00:00Z","timestamp":1748995200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>Malaria continues to be a severe health problem across the globe, especially within resource-limited areas which lack both skilled diagnostic personnel and diagnostic equipment. This study investigates the use of deep learning diagnosis for malaria through ConvNeXt models that incorporate transfer learning techniques with data augmentation methods for better model performance and transferability. A total number of 606276 thin blood smear images served as the final augmented dataset after the initial 27558 images underwent augmentation. The ConvNeXt Tiny model, version V1 Tiny, achieved an accuracy of 95.9%.; however, the upgraded V2 Tiny Remod version exceeded this benchmark, reaching 98.1% accuracy. The accuracy rate measured 61.4% for Swin Tiny, ResNet18 reached 62.6%, and ResNet50 obtained 81.4%. The combination of label smoothing with the AdamW optimiser produced a model which exhibited strong robustness as well as generalisability. The enhanced ConvNeXt V2 Tiny model combined with data augmentation, transfer learning techniques and explainability frameworks demonstrate a practical solution for malaria diagnosis that achieves high accuracy despite limitations of access to large datasets and microscopy expertise, often observed in resource-limited regions. The findings highlight the potential for real-time diagnostic applications in remote healthcare facilities and the viability of ConvNeXt models in enhancing malaria diagnosis globally.<\/jats:p>","DOI":"10.1371\/journal.pone.0313734","type":"journal-article","created":{"date-parts":[[2025,6,4]],"date-time":"2025-06-04T14:01:39Z","timestamp":1749045699000},"page":"e0313734","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":23,"title":["Application of ConvNeXt with transfer learning and data augmentation for malaria parasite detection in resource-limited settings using microscopic images"],"prefix":"10.1371","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-6763-0328","authenticated-orcid":true,"given":"Outlwile Pako","family":"Mmileng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Albert","family":"Whata","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Micheal","family":"Olusanya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8203-5984","authenticated-orcid":true,"given":"Siyabonga","family":"Mhlongo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2025,6,4]]},"reference":[{"key":"pone.0313734.ref001","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/978-3-031-15848-3_1","article-title":"Global health and malaria: Past and present.","volume-title":"Malarial drug delivery systems: Advances in treatment of infectious diseases. Advances in delivery science and technology","author":"S Sankineni","year":"2023"},{"key":"pone.0313734.ref002","volume-title":"World malaria report 2021","author":"WHO","year":"2021"},{"key":"pone.0313734.ref003","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1196\/annals.1425.037","article-title":"Malaria and poverty","volume":"1136","author":"A Teklehaimanot","year":"2008","journal-title":"Ann N Y Acad Sci"},{"issue":"4","key":"pone.0313734.ref004","first-page":"285","article-title":"A review on malarial parasite","volume":"10","author":"H Ullah","year":"2015","journal-title":"World J Zool"},{"issue":"2","key":"pone.0313734.ref005","doi-asserted-by":"crossref","first-page":"102","DOI":"10.3390\/tropicalmed5020102","article-title":"Evaluation of malaria diagnostic methods as a key for successful control and elimination programs","volume":"5","author":"A Mbanefo","year":"2020","journal-title":"Trop Med Infect Dis"},{"issue":"2","key":"pone.0313734.ref006","doi-asserted-by":"crossref","DOI":"10.1136\/bmjgh-2019-002023","article-title":"The quality of malaria care in 25 low-income and middle-income countries","volume":"5","author":"E Macarayan","year":"2020","journal-title":"BMJ Glob Health"},{"issue":"4","key":"pone.0313734.ref007","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1080\/00034989859401","article-title":"A review of practical techniques for the diagnosis of malaria","volume":"92","author":"MT Makler","year":"1998","journal-title":"Ann Trop Med Parasitol"},{"key":"pone.0313734.ref008","volume-title":"Microscopy for the detection, identification and quantification of malaria parasites on stained thick and thin blood films in research settings: procedure: methods manual","author":"E Ba","year":"2015"},{"key":"pone.0313734.ref009","doi-asserted-by":"crossref","first-page":"3811318","DOI":"10.1155\/2021\/3811318","article-title":"Challenges in laboratory diagnosis of malaria in a low-resource country at Tertiary Care in Eastern Nepal: a comparative study of conventional vs. molecular methodologies","volume":"2021","author":"P Dahal","year":"2021","journal-title":"J Trop Med"},{"key":"pone.0313734.ref010","doi-asserted-by":"crossref","first-page":"1006659","DOI":"10.3389\/fmicb.2022.1006659","article-title":"Advances and challenges in automated malaria diagnosis using digital microscopy imaging with artificial intelligence tools: A review","volume":"13","author":"CR Maturana","year":"2022","journal-title":"Front Microbiol"},{"key":"pone.0313734.ref011","doi-asserted-by":"crossref","first-page":"638182","DOI":"10.3389\/fonc.2021.638182","article-title":"Artificial Convolutional Neural Network in Object Detection and Semantic Segmentation for Medical Imaging Analysis","volume":"11","author":"R Yang","year":"2021","journal-title":"Front Oncol"},{"key":"pone.0313734.ref012","first-page":"221","volume-title":"A comparison of machine learning and deep learning in hyperspectral image classification. Enabling industry 40 through advances in mechatronics: Selected Articles from iM3F 2021, Malaysia","author":"FM Sadek","year":"2022"},{"key":"pone.0313734.ref013","volume-title":"CNN-based image analysis for malaria diagnosis. 2016 IEEE international conference on bioinformatics and biomedicine (BIBM)","author":"Z Liang","year":"2016"},{"key":"pone.0313734.ref014","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/978-3-030-33966-1_11","article-title":"Malaria disease detection using cnn technique with sgd, rmsprop and adam optimizers","volume-title":"Deep learning techniques for biomedical and health informatics","author":"A Kumar","year":"2020"},{"issue":"5","key":"pone.0313734.ref015","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1109\/JBHI.2020.3034863","article-title":"Clustering-based dual deep learning architecture for detecting red blood cells in malaria diagnostic smears","volume":"25","author":"YM Kassim","year":"2021","journal-title":"IEEE J Biomed Health Inform"},{"key":"pone.0313734.ref016","volume-title":"A convnet for the 2020s. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Z Liu","year":"2022"},{"key":"pone.0313734.ref017","volume-title":"ConvNext: A contemporary architecture for convolutional neural networks for Image classification. 2023 3rd International Conference on Innovative Sustainable Computational Technologies (CISCT)","author":"A Todi","year":"2023"},{"key":"pone.0313734.ref018","volume-title":"World malaria report 2023. Licence: CC BY-NC-SA 3.0 IGO","author":"WHO","year":"2023"},{"key":"pone.0313734.ref019","volume-title":"Malaria microscopy quality assurance manual-version 2","author":"WHO","year":"2016"},{"key":"pone.0313734.ref020","doi-asserted-by":"crossref","first-page":"7324281","DOI":"10.1155\/2022\/7324281","article-title":"Impact of malaria diagnostic technologies on the disease burden in the sub-Saharan Africa","volume":"2022","author":"J Wambani","year":"2022","journal-title":"J Trop Med"},{"issue":"5","key":"pone.0313734.ref021","doi-asserted-by":"crossref","first-page":"1427","DOI":"10.1109\/JBHI.2019.2939121","article-title":"Deep Learning for Smartphone-Based Malaria Parasite Detection in Thick Blood Smears","volume":"24","author":"F Yang","year":"2020","journal-title":"IEEE J Biomed Health Inform"},{"issue":"5","key":"pone.0313734.ref022","doi-asserted-by":"crossref","first-page":"1299","DOI":"10.1109\/TMI.2016.2535302","article-title":"Convolutional neural networks for medical image analysis: full training or fine tuning?","volume":"35","author":"N Tajbakhsh","year":"2016","journal-title":"IEEE Trans Med Imaging"},{"issue":"1","key":"pone.0313734.ref023","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1186\/s40537-021-00444-8","article-title":"Review of deep learning: concepts, CNN architectures, challenges, applications, future directions","volume":"8","author":"L Alzubaidi","year":"2021","journal-title":"J Big Data"},{"key":"pone.0313734.ref024","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3502287","article-title":"A systematic review on data scarcity problem in deep learning: solution and applications","volume":"54","author":"MA Bansal","year":"2022","journal-title":"ACM Comput Surv"},{"key":"pone.0313734.ref025","author":"HM Zhuang Liu","year":"2022"},{"key":"pone.0313734.ref026","article-title":"A study of convnext architectures for enhanced image captioning","author":"L Ramos","year":"2024","journal-title":"IEEE Access"},{"key":"pone.0313734.ref027","volume-title":"ConvNeXt V2: Co-designing and scaling convNets with masked autoencoders 2023","author":"SD Sanghyun Woo"},{"key":"pone.0313734.ref028","unstructured":"Li J, Wang C, Huang B, Zhou Z. ConvNeXt-backbone HoVerNet for nuclei segmentation and classification. arXiv preprint arXiv:220213560. 2022."},{"key":"pone.0313734.ref029","volume-title":"Swin transformer: Hierarchical vision transformer using shifted windows. Conference: 2021 IEEE\/CVF International Conference on Computer Vision (ICCV); Montreal, QC, Canada: Institute of Electrical and Electronics Engineers (IEEE)","author":"Z Liu","year":"2021"},{"key":"pone.0313734.ref030","article-title":"Improved deep learning image classification algorithm based on Swin Transformer V2","volume":"9","author":"J Wei","year":"2023","journal-title":"PeerJ Comput Sci"},{"key":"pone.0313734.ref031","volume-title":"Making vision transformers truly shift-equivariant. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"RA Rojas-Gomez","year":"2024"},{"issue":"141","key":"pone.0313734.ref032","doi-asserted-by":"crossref","first-page":"20170387","DOI":"10.1098\/rsif.2017.0387","article-title":"Opportunities and obstacles for deep learning in biology and medicine","volume":"15","author":"T Ching","year":"2018","journal-title":"J R Soc Interface"},{"key":"pone.0313734.ref033","doi-asserted-by":"crossref","first-page":"102158","DOI":"10.1016\/j.artmed.2021.102158","article-title":"The three ghosts of medical AI: Can the black-box present deliver?","volume":"124","author":"TP Quinn","year":"2022","journal-title":"Artif Intell Med"},{"key":"pone.0313734.ref034","unstructured":"Touvron H, Lavril T, Izacard G, Martinet X, Lachaux M-A, Lacroix T, et al. Llama: Open and efficient foundation language models. arXiv preprint arXiv:230213971. 2023."},{"issue":"10","key":"pone.0313734.ref035","doi-asserted-by":"crossref","first-page":"239","DOI":"10.3390\/jimaging10100239","article-title":"A Survey on explainable artificial intelligence (XAI) techniques for visualizing deep learning models in medical imaging","volume":"10","author":"D Bhati","year":"2024","journal-title":"J Imaging"},{"key":"pone.0313734.ref036","first-page":"137","article-title":"XAI for medical image segmentation in medical decision support systems","volume":"50","author":"AG Akpan","year":"2022","journal-title":"Explainable Artificial Intelligence in Medical Decision Support Systems"},{"issue":"8","key":"pone.0313734.ref037","first-page":"8052","article-title":"Generalizing to unseen domains: A survey on domain generalization","volume":"35","author":"J Wang","year":"2022","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"1","key":"pone.0313734.ref038","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-014-0007-7","article-title":"Deep learning applications and challenges in big data analytics","volume":"2","author":"MM Najafabadi","year":"2015","journal-title":"J Big Data"},{"issue":"3","key":"pone.0313734.ref039","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1016\/j.ejor.2013.09.036","article-title":"Recent advances in robust optimization: An overview","volume":"235","author":"V Gabrel","year":"2014","journal-title":"European Journal of Operational Research"},{"key":"pone.0313734.ref040","volume-title":"Towards understanding label smoothing. arXiv preprint arXiv:200611653","author":"Y Xu","year":"2020"},{"key":"pone.0313734.ref041","first-page":"32","volume-title":"When does label smoothing help? Advances in neural information processing systems","author":"R M\u00fcller","year":"2019"},{"key":"pone.0313734.ref042","volume-title":"Does label smoothing mitigate label noise? International Conference on Machine Learning","author":"M Lukasik","year":"2020"},{"issue":"23","key":"pone.0313734.ref043","doi-asserted-by":"crossref","first-page":"17095","DOI":"10.1007\/s00521-023-08568-z","article-title":"A modified Adam algorithm for deep neural network optimization","volume":"35","author":"M Reyad","year":"2023","journal-title":"Neural Comput & Applic"},{"key":"pone.0313734.ref044","unstructured":"Pan Y, Li Y. Toward understanding why adam converges faster than sgd for transformers. arXiv preprint arXiv:230600204. 2023."},{"issue":"3","key":"pone.0313734.ref045","first-page":"25","article-title":"A machine learning approach for face mask detection system with AdamW optimizer","volume":"7","author":"LK Meng","year":"2023","journal-title":"J Appl Technol Innov"},{"key":"pone.0313734.ref046","doi-asserted-by":"crossref","first-page":"101693","DOI":"10.1016\/j.media.2020.101693","article-title":"Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation","volume":"63","author":"N Tajbakhsh","year":"2020","journal-title":"Med Image Anal"},{"issue":"1","key":"pone.0313734.ref047","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/JPROC.2020.3004555","article-title":"A comprehensive survey on transfer learning","volume":"109","author":"F Zhuang","year":"2021","journal-title":"Proc IEEE"},{"issue":"330","key":"pone.0313734.ref048","first-page":"23","article-title":"Transfer learning from pre-trained models","volume":"10","author":"P Marcelino","year":"2018","journal-title":"Towards Data Science"},{"issue":"1","key":"pone.0313734.ref049","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1186\/s40537-022-00652-w","article-title":"Transfer learning: a friendly introduction","volume":"9","author":"A Hosna","year":"2022","journal-title":"J Big Data"},{"issue":"24","key":"pone.0313734.ref050","doi-asserted-by":"crossref","first-page":"18069","DOI":"10.1007\/s00521-019-04051-w","article-title":"The importance of interpretability and visualization in machine learning for applications in medicine and health care","volume":"32","author":"A Vellido","year":"2019","journal-title":"Neural Comput Applic"},{"issue":"5","key":"pone.0313734.ref051","doi-asserted-by":"crossref","DOI":"10.1002\/widm.1379","article-title":"Interpretability of machine learning\u2010based prediction models in healthcare","volume":"10","author":"G Stiglic","year":"2020","journal-title":"WIREs Data Min Knowl"},{"issue":"9","key":"pone.0313734.ref052","doi-asserted-by":"crossref","first-page":"5521","DOI":"10.3390\/app13095521","article-title":"Comparing vision transformers and convolutional neural networks for image classification: a literature review","volume":"13","author":"J Maur\u00edcio","year":"2023","journal-title":"Applied Sciences"},{"key":"pone.0313734.ref053","first-page":"12116","article-title":"Do vision transformers see like convolutional neural networks?","volume":"34","author":"M Raghu","year":"2021","journal-title":"Adv Neural Inf Process Syst"},{"key":"pone.0313734.ref054","volume-title":"Performance assessment of advanced cnn and transformer architectures in skin cancer detection. 2024 11th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)","author":"MSI Sajol","year":"2024"},{"issue":"1","key":"pone.0313734.ref055","doi-asserted-by":"crossref","first-page":"25429","DOI":"10.1038\/s41598-024-75748-9","article-title":"Periapical lesion detection in periapical radiographs using the latest convolutional neural network ConvNeXt and its integrated models","volume":"14","author":"J Liu","year":"2024","journal-title":"Sci Rep"},{"issue":"8","key":"pone.0313734.ref056","doi-asserted-by":"crossref","first-page":"2425","DOI":"10.3390\/s24082425","article-title":"FNeXter: A Multi-Scale Feature Fusion Network Based on ConvNeXt and Transformer for Retinal OCT Fluid Segmentation","volume":"24","author":"Z Niu","year":"2024","journal-title":"Sensors (Basel)"},{"key":"pone.0313734.ref057","volume-title":"NLM - Malaria Data","author":"Medicine NLo","year":"2018"},{"issue":"1","key":"pone.0313734.ref058","doi-asserted-by":"crossref","first-page":"825","DOI":"10.1186\/s12879-020-05453-1","article-title":"Malaria Screener: a smartphone application for automated malaria screening","volume":"20","author":"H Yu","year":"2020","journal-title":"BMC Infect Dis"},{"issue":"7","key":"pone.0313734.ref059","doi-asserted-by":"crossref","first-page":"4785","DOI":"10.1007\/s10994-023-06326-9","article-title":"Understanding CNN fragility when learning with imbalanced data","volume":"113","author":"D Dablain","year":"2023","journal-title":"Mach Learn"},{"issue":"2","key":"pone.0313734.ref060","doi-asserted-by":"crossref","first-page":"5","DOI":"10.5120\/1140-1493","article-title":"A Theory Based on Conversion of RGB image to Gray image","volume":"7","author":"T Kumar","year":"2010","journal-title":"IJCA"},{"key":"pone.0313734.ref061","article-title":"Infected cell identification in thin blood images based on color pixel classification: comparison and analysis","volume-title":"Progress in pattern recognition, image analysis and applications: 12th Iberoamericann Congress on Pattern Recognition, CIARP 2007, Valparaiso, Chile, November 13-16, 2007 Proceedings 12","author":"G D\u00edaz","year":"2007"},{"issue":"1","key":"pone.0313734.ref062","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1007\/s12639-019-01163-x","article-title":"Automatic detection of Plasmodium parasites from microscopic blood images","volume":"44","author":"T Fatima","year":"2020","journal-title":"J Parasit Dis"},{"issue":"6","key":"pone.0313734.ref063","doi-asserted-by":"crossref","first-page":"803","DOI":"10.1007\/s40846-015-0101-0","article-title":"Parasite and Infected-Erythrocyte Image Segmentation in Stained Blood Smears","volume":"35","author":"Y-W Hung","year":"2015","journal-title":"J Med Biol Eng"},{"key":"pone.0313734.ref064","first-page":"86","article-title":"Automatic malaria diagnosis by the use of multispectral contrast imaging","volume":"75","author":"O Bagui","year":"2015","journal-title":"J Phys Chem News"},{"issue":"2","key":"pone.0313734.ref065","first-page":"123","article-title":"Detection of malarial parasite in blood using image processing","volume":"10","author":"H Sharma","year":"2023","journal-title":"J Med Imaging"},{"issue":"3","key":"pone.0313734.ref066","doi-asserted-by":"crossref","first-page":"239","DOI":"10.3390\/dermatopathology11030026","article-title":"Enhancing Melanoma Diagnosis with Advanced Deep Learning Models Focusing on Vision Transformer, Swin Transformer, and ConvNeXt","volume":"11","author":"S Aksoy","year":"2024","journal-title":"Dermatopathology (Basel)"},{"key":"pone.0313734.ref067","volume-title":"Impact of image resizing on deep learning detectors for training time and model performance. International Conference on Applications in Electronics Pervading Industry, Environment and Society","author":"S Saponara","year":"2021"},{"issue":"3","key":"pone.0313734.ref068","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1080\/21681163.2019.1683469","article-title":"Medical image interpolation based on 3D lanczos filtering","volume":"8","author":"T Moraes","year":"2020","journal-title":"Comput Methods Biomech Biomed Engin Imaging Vis"},{"key":"pone.0313734.ref069","unstructured":"Magnusson A, Sandahl A. Artifact-free image upscaling: an evaluation of an artifact-free color interpolation algorithm with respect to visual quality. 2022."},{"key":"pone.0313734.ref070","volume-title":"Lanczos resampling for the digital processing of remotely sensed images. Proceedings of International Conference on VLSI, Communication, Advanced Devices, Signals & Systems and Networking (VCASAN-2013)","author":"B Madhukar","year":"2013"},{"key":"pone.0313734.ref071","first-page":"337","article-title":"Fast discrete sinc-interpolation: a gold standard for image resampling","volume":"7","author":"L Yaroslavsky","year":"2007","journal-title":"Advances in Signal Transforms: Theory and Applications"},{"issue":"937","key":"pone.0313734.ref072","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1086\/675812","article-title":"Resampling Images in Fourier Domain","volume":"126","author":"GM Bernstein","year":"2014","journal-title":"Publications of the Astronomical Society of the Pacific"},{"key":"pone.0313734.ref073","doi-asserted-by":"crossref","unstructured":"Th\u00e9venaz P, Blu T, Unser M. Image interpolation and resampling. Handbook of medical imaging, processing and analysis. 2000;1(1):393\u2013420.","DOI":"10.1016\/B978-012077790-7\/50030-8"},{"issue":"3","key":"pone.0313734.ref074","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1145\/3446776","article-title":"Understanding deep learning (still) requires rethinking generalization","volume":"64","author":"C Zhang","year":"2021","journal-title":"Commun ACM"},{"key":"pone.0313734.ref075","volume-title":"Improving the robustness of deep neural networks via stability training. Proceedings of the ieee conference on computer vision and pattern recognition","author":"S Zheng","year":"2016"},{"key":"pone.0313734.ref076","volume-title":"Improving malaria parasite detection from red blood cell using deep convolutional neural networks","author":"A Rahman","year":"2019"},{"issue":"8","key":"pone.0313734.ref077","first-page":"14","article-title":"Advancements in data augmentation and transfer learning: A comprehensive survey to address data scarcity challenges","volume":"17","author":"S Fayaz","year":"2024","journal-title":"Recent advances in computer science and communications (Formerly: Recent patents on computer science)"},{"key":"pone.0313734.ref078","volume-title":"Data augmentation for improving deep learning in image classification problem. 2018 international interdisciplinary PhD workshop (IIPhDW)","author":"A Miko\u0142ajczyk","year":"2018"},{"issue":"7","key":"pone.0313734.ref079","first-page":"483","article-title":"Data augmentation techniques applied to medical images","volume":"5","author":"Y Chandola","year":"2024","journal-title":"Int J Res Publ Rev"},{"issue":"1","key":"pone.0313734.ref080","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-019-0197-0","article-title":"A survey on image data augmentation for deep learning","volume":"6","author":"C Shorten","year":"2019","journal-title":"J Big Data"},{"key":"pone.0313734.ref081","doi-asserted-by":"crossref","first-page":"106056","DOI":"10.1016\/j.mimet.2020.106056","article-title":"Automatic Bacillus anthracis bacteria detection and segmentation in microscopic images using UNet+","volume":"177","author":"F Hoorali","year":"2020","journal-title":"J Microbiol Methods"},{"issue":"11","key":"pone.0313734.ref082","doi-asserted-by":"crossref","first-page":"2309378","DOI":"10.1002\/adfm.202309378","article-title":"Bio\u2010inspired artificial retinas based on a fibrous inorganic\u2013organic heterostructure for neuromorphic vision","volume":"34","author":"T Trung","year":"2024","journal-title":"Adv Funct Mater"},{"key":"pone.0313734.ref083","doi-asserted-by":"crossref","first-page":"128123","DOI":"10.1016\/j.neucom.2024.128123","article-title":"Data augmentation for Gram-stain images based on Vector Quantized Variational AutoEncoder","volume":"600","author":"V Shwetha","year":"2024","journal-title":"Neurocomputing"},{"key":"pone.0313734.ref084","doi-asserted-by":"crossref","first-page":"392","DOI":"10.4018\/979-8-3693-1634-4.ch023","article-title":"Brain tumor classification from magnetic resonance imaging using deep learning and novel data augmentation.","volume-title":"Transformational interventions for business, technology, and healthcare","author":"N Tiwari","year":"2023"},{"key":"pone.0313734.ref085","volume-title":"Assessing the importance of colours for cnns in object recognition. arXiv preprint arXiv:201206917","author":"A Singh","year":"2020"},{"key":"pone.0313734.ref086","first-page":"167","volume-title":"Data augmentation in training deep learning models for medical image analysis. Deep learners and deep learner descriptors for medical applications","author":"B Abdollahi","year":"2020"},{"issue":"5","key":"pone.0313734.ref087","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1111\/1754-9485.13261","article-title":"A review of medical image data augmentation techniques for deep learning applications","volume":"65","author":"P Chlap","year":"2021","journal-title":"J Med Imaging Radiat Oncol"},{"key":"pone.0313734.ref088","doi-asserted-by":"crossref","first-page":"15191","DOI":"10.1109\/ACCESS.2021.3050758","article-title":"Local augment: utilizing local bias property of convolutional neural networks for data augmentation","volume":"9","author":"Y Kim","year":"2021","journal-title":"IEEE Access"},{"key":"pone.0313734.ref089","volume-title":"Deep image clustering with category-style representation. Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, August 23\u201328, 2020, Proceedings, Part XIV 16","author":"J Zhao","year":"2020"},{"issue":"3","key":"pone.0313734.ref090","first-page":"034501","article-title":"Understanding the learned behavior of customized convolutional neural networks toward malaria parasite detection in thin blood smear images","volume":"5","author":"S Rajaraman","year":"2018","journal-title":"J Med Imaging (Bellingham)"},{"key":"pone.0313734.ref091","doi-asserted-by":"crossref","first-page":"100258","DOI":"10.1016\/j.array.2022.100258","article-title":"Data augmentation: A comprehensive survey of modern approaches","volume":"16","author":"A Mumuni","year":"2022","journal-title":"Array"},{"key":"pone.0313734.ref092","volume-title":"Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis. Conference: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR); New Orleans, Louisiana","author":"Y Tang","year":"2022"},{"key":"pone.0313734.ref093","volume-title":"Improving vision transformers on small datasets by increasing input information density in frequency domain. IEEE\/CVF International Conference on Computer Vision Workshops (ICCVW)","author":"X Chen","year":"2022"},{"key":"pone.0313734.ref094","volume-title":"High Performance Target Detection Based on Swin Transformer. 2024 2nd International Conference on Signal Processing and Intelligent Computing (SPIC)","author":"Z Liang","year":"2024"},{"key":"pone.0313734.ref095","doi-asserted-by":"crossref","unstructured":"Aburass S, Dorgham O. Performance evaluation of swin vision transformer model using gradient accumulation optimization technique. 2023.","DOI":"10.1007\/978-3-031-47448-4_5"},{"key":"pone.0313734.ref096","volume-title":"Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition","author":"K He","year":"2016"},{"key":"pone.0313734.ref097","doi-asserted-by":"crossref","first-page":"108152","DOI":"10.1109\/ACCESS.2019.2931922","article-title":"Moving Object Detection Method via ResNet-18 With Encoder\u2013Decoder Structure in Complex Scenes","volume":"7","author":"X Ou","year":"2019","journal-title":"IEEE Access"},{"key":"pone.0313734.ref098","volume-title":"Malaria parasite detection from human blood smear images using deep learning techniques","author":"YJ Teow","year":"2023"},{"issue":"1","key":"pone.0313734.ref099","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1007\/s10238-024-01379-z","article-title":"Application of image recognition technology in pathological diagnosis of blood smears","volume":"24","author":"W Cheng","year":"2024","journal-title":"Clin Exp Med"},{"key":"pone.0313734.ref100","volume-title":"Convnext v2: Co-designing and scaling convnets with masked autoencoders. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"S Woo","year":"2023"},{"key":"pone.0313734.ref101","volume-title":"An investigation of how label smoothing affects generalization. arXiv preprint arXiv:201012648","author":"B Chen","year":"2020"},{"key":"pone.0313734.ref102","volume-title":"Evolved optimizer for vision. First Conference on Automated Machine Learning (Late-Breaking Workshop)","author":"X Chen","year":"2022"},{"key":"pone.0313734.ref103","first-page":"1","article-title":"Tackling cold-start with deep personalized transfer of user preferences for cross-domain recommendation","author":"S Omidvar","year":"2023","journal-title":"International Journal of Data Science and Analytics"},{"issue":"1","key":"pone.0313734.ref104","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-016-0043-6","article-title":"A survey of transfer learning","volume":"3","author":"K Weiss","year":"2016","journal-title":"J Big Data"},{"key":"pone.0313734.ref105","article-title":"Transfusion: understanding transfer learning for medical imaging","volume":"32","author":"M Raghu","year":"2019","journal-title":"Adv Neural Inf Process Syst"},{"issue":"10","key":"pone.0313734.ref106","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A Survey on Transfer Learning","volume":"22","author":"SJ Pan","year":"2010","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"pone.0313734.ref107","volume-title":"Swin transformer: hierarchical vision transformer using shifted windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV)","author":"Liu ZaL, Yutong and Cao, Yue and Hu, Han and Wei, Yixuan and Zhang, Zheng and Lin, Stephen and Guo, Baining","year":"2021"},{"issue":"1","key":"pone.0313734.ref108","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1109\/TPAMI.2022.3152247","article-title":"A Survey on Vision Transformer","volume":"45","author":"K Han","year":"2023","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"pone.0313734.ref109","first-page":"25","volume-title":"Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems","author":"A Krizhevsky","year":"2012"},{"issue":"1","key":"pone.0313734.ref110","first-page":"147","article-title":"Empirical analysis of a fine-tuned deep convolutional model in classifying and detecting malaria parasites from blood smears","volume":"15","author":"F Montalbo","year":"2021","journal-title":"KSII Trans Internet Inf Syst"},{"key":"pone.0313734.ref111","doi-asserted-by":"crossref","first-page":"57627","DOI":"10.1109\/ACCESS.2023.3284388","article-title":"Impact of Mixed Precision Techniques on Training and Inference Efficiency of Deep Neural Networks","volume":"11","author":"M D\u00f6rrich","year":"2023","journal-title":"IEEE Access"},{"key":"pone.0313734.ref112","volume-title":"Accelerating neural network training: A brief review. Proceedings of the 2024 8th International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence","author":"S Nokhwal","year":"2024"},{"issue":"9","key":"pone.0313734.ref113","doi-asserted-by":"crossref","first-page":"92","DOI":"10.3390\/jimaging6090092","article-title":"Comparative Study of First Order Optimizers for Image Classification Using Convolutional Neural Networks on Histopathology Images","volume":"6","author":"I Kandel","year":"2020","journal-title":"J Imaging"},{"key":"pone.0313734.ref114","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-030-58817-5_20","article-title":"Parameter tuning using adaptive moment estimation in deep learning neural networks","volume-title":"Computational Science and Its Applications\u2013ICCSA 2020: 20th International Conference, Cagliari, Italy, July 1\u20134, 2020, Proceedings, Part VI 20","author":"E Okewu","year":"2020"},{"key":"pone.0313734.ref115","author":"H Lee","year":"2023"},{"issue":"7553","key":"pone.0313734.ref116","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"Y LeCun","year":"2015","journal-title":"Nature"},{"key":"pone.0313734.ref117","article-title":"Switch EMA: A Free Lunch for Better Flatness and Sharpness","author":"S Li","year":"2024","journal-title":"arXiv preprint arXiv:240209240"},{"key":"pone.0313734.ref118","article-title":"Adaptive learning rates for faster stochastic gradient methods","author":"S Horv\u00e1th","year":"2022","journal-title":"arXiv preprint arXiv:220805287"},{"key":"pone.0313734.ref119","article-title":"Reducing underflow in mixed precision training by gradient scaling","author":"R Zhao","year":"2021","journal-title":"Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence"},{"key":"pone.0313734.ref120","article-title":"Automated learning rate scheduler for large-batch training","author":"C Kim","year":"2021","journal-title":"arXiv preprint arXiv:210705855"},{"key":"pone.0313734.ref121","doi-asserted-by":"crossref","DOI":"10.1145\/3377930.3390158","volume-title":"Autolr: An evolutionary approach to learning rate policies. Proceedings of the 2020 genetic and evolutionary computation conference","author":"P Carvalho","year":"2020"},{"issue":"10","key":"pone.0313734.ref122","article-title":"Binary cross entropy with deep learning technique for image classification","volume":"9","author":"U Ruby","year":"2020","journal-title":"Int J Adv Trends Comput Sci Eng"},{"key":"pone.0313734.ref123","doi-asserted-by":"crossref","first-page":"4806","DOI":"10.1109\/ACCESS.2019.2962617","article-title":"The Real-World-Weight Cross-Entropy Loss Function: Modeling the Costs of Mislabeling","volume":"8","author":"Y Ho","year":"2020","journal-title":"IEEE Access"},{"key":"pone.0313734.ref124","volume-title":"Symmetric cross entropy for robust learning with noisy labels. Proceedings of the IEEE\/CVF international conference on computer vision","author":"Y Wang","year":"2019"},{"key":"pone.0313734.ref125","volume-title":"Malaria Diagnosis App 1.0 Johannesburg","author":"OP Mmileng","year":"2024"},{"key":"pone.0313734.ref126","volume-title":"Explaining the explainer: A first theoretical analysis of LIME. International conference on artificial intelligence and statistics","author":"D Garreau","year":"2020"},{"issue":"11","key":"pone.0313734.ref127","doi-asserted-by":"crossref","first-page":"1932","DOI":"10.3390\/diagnostics13111932","article-title":"Explainable AI for Retinoblastoma Diagnosis: Interpreting Deep Learning Models with LIME and SHAP","volume":"13","author":"B Aldughayfiq","year":"2023","journal-title":"Diagnostics (Basel)"},{"key":"pone.0313734.ref128","article-title":"The llama 3 herd of models","author":"A Dubey","year":"2024","journal-title":"arXiv preprint arXiv:240721783"},{"key":"pone.0313734.ref129","author":"R Vavekanand","year":"2024"},{"issue":"2","key":"pone.0313734.ref130","doi-asserted-by":"crossref","first-page":"18","DOI":"10.48185\/smhs.v1i2.1225","article-title":"Ensemble deep learning and machine learning: applications, opportunities, challenges, and future directions","volume":"1","author":"N Rane","year":"2024","journal-title":"SMHS"},{"issue":"4","key":"pone.0313734.ref131","article-title":"Interpretation and visualization techniques for deep learning models in medical imaging","volume":"66","author":"DT Huff","year":"2021","journal-title":"Phys Med Biol"},{"key":"pone.0313734.ref132","volume-title":"Improving estimation accuracy prediction of software development effort: A proposed ensemble model. 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE)","author":"Y Mahmood","year":"2020"},{"key":"pone.0313734.ref133","doi-asserted-by":"crossref","first-page":"105138","DOI":"10.1016\/j.compbiomed.2021.105138","article-title":"Accuracy versus reliability-based modelling approaches for medical decision making","volume":"141","author":"S Etemadi","year":"2022","journal-title":"Comput Biol Med"},{"key":"pone.0313734.ref134","volume-title":"Evaluation measures of the classification performance of imbalanced data sets. Computational Intelligence and Intelligent Systems: 4th International Symposium, ISICA 2009, Huangshi, China, October 23-25, 2009 Proceedings 4","author":"Q Gu","year":"2009"},{"key":"pone.0313734.ref135","volume-title":"Deep learning based-recommendation system: an overview on models, datasets, evaluation metrics, and future trends. Proceedings of the 2019 2nd international conference on computational intelligence and intelligent systems","author":"K Ong","year":"2019"},{"key":"pone.0313734.ref136","volume-title":"CloudCmp: comparing public cloud providers. Proceedings of the 10th ACM SIGCOMM conference on Internet measurement","author":"A Li","year":"2010"},{"key":"pone.0313734.ref137","volume-title":"Google Cloud Platform for Architects: Design and manage powerful cloud solutions","author":"V Srinivasan","year":"2018"},{"key":"pone.0313734.ref138","volume-title":"Cloud application architectures: building applications and infrastructure in the cloud","author":"G Reese","year":"2009"}],"container-title":["PLOS One"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0313734","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,4]],"date-time":"2025-06-04T14:02:21Z","timestamp":1749045741000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0313734"}},"subtitle":[],"editor":[{"given":"Hirenkumar Kantilal","family":"Mewada","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2025,6,4]]},"references-count":138,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,6,4]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0313734","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2024.10.31.24316549","asserted-by":"object"}]},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,4]]}}}