{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T06:06:37Z","timestamp":1791957997033,"version":"4.3.4"},"reference-count":48,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,10,27]],"date-time":"2021-10-27T00:00:00Z","timestamp":1635292800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010269","name":"Wellcome Trust","doi-asserted-by":"publisher","award":["220211"],"award-info":[{"award-number":["220211"]}],"id":[{"id":"10.13039\/100010269","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006229","name":"ORISE","doi-asserted-by":"publisher","award":["DE-SC0014664"],"award-info":[{"award-number":["DE-SC0014664"]}],"id":[{"id":"10.13039\/100006229","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>We propose a new framework, PlasmodiumVF-Net, to analyze thick smear microscopy images for a malaria diagnosis on both image and patient-level. Our framework detects whether a patient is infected, and in case of a malarial infection, reports whether the patient is infected by Plasmodium falciparum or Plasmodium vivax. PlasmodiumVF-Net first detects candidates for Plasmodium parasites using a Mask Regional-Convolutional Neural Network (Mask R-CNN), filters out false positives using a ResNet50 classifier, and then follows a new approach to recognize parasite species based on a score obtained from the number of detected patches and their aggregated probabilities for all of the patient images. Reporting a patient-level decision is highly challenging, and therefore reported less often in the literature, due to the small size of detected parasites, the similarity to staining artifacts, the similarity of species in different development stages, and illumination or color variations on patient-level. We use a manually annotated dataset consisting of 350 patients, with about 6000 images, which we make publicly available together with this manuscript. Our framework achieves an overall accuracy above 90% on image and patient-level.<\/jats:p>","DOI":"10.3390\/diagnostics11111994","type":"journal-article","created":{"date-parts":[[2021,10,27]],"date-time":"2021-10-27T22:00:23Z","timestamp":1635372023000},"page":"1994","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["Diagnosing Malaria Patients with Plasmodium falciparum and vivax Using Deep Learning for Thick Smear Images"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5339-8081","authenticated-orcid":false,"given":"Yasmin M.","family":"Kassim","sequence":"first","affiliation":[{"name":"National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng","family":"Yang","sequence":"additional","affiliation":[{"name":"National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hang","family":"Yu","sequence":"additional","affiliation":[{"name":"National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5355-0562","authenticated-orcid":false,"given":"Richard J.","family":"Maude","sequence":"additional","affiliation":[{"name":"Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Bangkok 10400, Thailand"},{"name":"Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7LG, UK"},{"name":"Harvard TH Chan School of Public Health, Harvard University, Boston, MA 02115, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6877-4318","authenticated-orcid":false,"given":"Stefan","family":"Jaeger","sequence":"additional","affiliation":[{"name":"National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,27]]},"reference":[{"key":"ref_1","unstructured":"WHO (2020). World Malaria Report 2020, World Health Organization."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"93","DOI":"10.3347\/kjp.2009.47.2.93","article-title":"Malaria diagnosis: A brief review","volume":"47","author":"Tangpukdee","year":"2009","journal-title":"Korean J. Parasitol."},{"key":"ref_3","unstructured":"WHO (2015). Guidelines for the Treatment of Malaria, World Health Organization."},{"key":"ref_4","unstructured":"Bloland, P.B., and World Health Organization (2001). Drug Resistance in Malaria, World Health Organization. Technical Report."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"94","DOI":"10.7861\/futurehosp.6-2-94","article-title":"The potential for artificial intelligence in healthcare","volume":"6","author":"Davenport","year":"2019","journal-title":"Future Healthc. J."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zemouri, R., Zerhouni, N., and Racoceanu, D. (2019). Deep learning in the biomedical applications: Recent and future status. Appl. Sci., 9.","DOI":"10.3390\/app9081526"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1016\/j.eng.2019.08.015","article-title":"Artificial intelligence in healthcare: Review and prediction case studies","volume":"6","author":"Rong","year":"2020","journal-title":"Engineering"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.trsl.2017.12.004","article-title":"Image analysis and machine learning for detecting malaria","volume":"194","author":"Poostchi","year":"2018","journal-title":"Transl. Res."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/jmi.12270","article-title":"Computational microscopic imaging for malaria parasite detection: A systematic review","volume":"260","author":"Das","year":"2015","journal-title":"J. Microsc."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"e4568","DOI":"10.7717\/peerj.4568","article-title":"Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images","volume":"6","author":"Rajaraman","year":"2018","journal-title":"PeerJ"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Kassim, Y.M., and Jaeger, S. (2020, January 13\u201315). A Cell Augmentation Tool for Blood Smear Analysis. Proceedings of the IEEE Applied Imagery Pattern Recognition Workshop, Washington, DC, USA.","DOI":"10.1109\/AIPR50011.2020.9425053"},{"key":"ref_12","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":"Kassim","year":"2021","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1007\/s10916-015-0280-9","article-title":"Blood smear image based malaria parasite and infected-erythrocyte detection and segmentation","volume":"39","author":"Tsai","year":"2015","journal-title":"J. Med. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"May, Z., Aziz, S.S.A.M., and Salamat, R. (2013, January 8\u201310). Automated quantification and classification of malaria parasites in thin blood smears. Proceedings of the IEEE International Conference on Signal and Image Processing Applications, Melaka, Malaysia.","DOI":"10.1109\/ICSIPA.2013.6708035"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"15297","DOI":"10.1007\/s11042-019-7162-y","article-title":"Deep learning approach to detect malaria from microscopic images","volume":"79","author":"Vijayalakshmi","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2009","DOI":"10.1128\/JCM.02562-16","article-title":"Update on malaria diagnostics and test utilization","volume":"55","author":"Mathison","year":"2017","journal-title":"J. Clin. Microbiol."},{"key":"ref_17","first-page":"435","article-title":"Detection and classification device for malaria parasites in thick-blood films","volume":"1","author":"Kaewkamnerd","year":"2011","journal-title":"IEEE Int. Conf. Intell. Data Acquis. Adv. Comput. Syst."},{"key":"ref_18","unstructured":"Elter, M., Ha\u00dflmeyer, E., and Zerfa\u00df, T. (September, January 30). Detection of malaria parasites in thick blood films. Proceedings of the International conference of the IEEE Engineering in Medicine and Biology Society, Boston, MA, USA."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Purnama, I.K.E., Rahmanti, F.Z., and Purnomo, M.H. (2013, January 7\u20138). Malaria parasite identification on thick blood film using genetic programming. Proceedings of the International Conference on Instrumentation, Communications, Information Technology and Biomedical Engineering, Bandung, Indonesia.","DOI":"10.1109\/ICICI-BME.2013.6698491"},{"key":"ref_20","first-page":"115","article-title":"Automated blood smear analysis for mobile malaria diagnosis","volume":"31","author":"Quinn","year":"2014","journal-title":"Mob. Point Care Monit. Diagn. Device Des."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"645","DOI":"10.4172\/2157-7420.1000179","article-title":"A combined algorithm for malaria detection from thick smear blood slides","volume":"6","author":"Chakrabortya","year":"2015","journal-title":"J. Health Med. Inform."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Delahunt, C.B., Mehanian, C., Hu, L., McGuire, S.K., Champlin, C.R., Horning, M.P., Wilson, B.K., and Thompon, C.M. (2015, January 8\u201311). Automated microscopy and machine learning for expert-level malaria field diagnosis. Proceedings of the IEEE Global Humanitarian Technology Conference, Seattle, WA, USA.","DOI":"10.1109\/GHTC.2015.7344002"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.procs.2016.07.024","article-title":"Automated Detection of Malaria Parasites on Thick Blood Smears via Mobile Devices","volume":"90","author":"Rosado","year":"2016","journal-title":"Procedia Comput. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Dave, I.R., and Upla, K.P. (2017, January 2\u20133). Computer aided diagnosis of malaria disease for thin and thick blood smear microscopic images. Proceedings of the International Conference on Signal Processing and Integrated Networks, Noida, India.","DOI":"10.1109\/SPIN.2017.8050013"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Mehanian, C., Jaiswal, M., Delahunt, C., Thompson, C., Horning, M., Hu, L., McGuire, S., Ostbye, T., Mehanian, M., and Wilson, B. (2017, January 22\u201329). Computer-Automated Malaria Diagnosis and Quantitation Using Convolutional Neural Networks. Proceedings of the IEEE International Conference on Computer Vision Workshops, Venice, Italy.","DOI":"10.1109\/ICCVW.2017.22"},{"key":"ref_26","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":"Yang","year":"2019","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"763","DOI":"10.1007\/s10278-019-00284-2","article-title":"Real-time malaria parasite screening in thick blood smears for low-resource setting","volume":"33","author":"Chibuta","year":"2020","journal-title":"J. Digit. Imaging"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Abdurahman, F., Fante, K.A., and Aliy, M. (2021). Malaria parasite detection in thick blood smear microscopic images using modified YOLOV3 and YOLOV4 models. BMC Bioinform., 22.","DOI":"10.1186\/s12859-021-04036-4"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1186\/s12936-021-03631-3","article-title":"Performance of a fully-automated system on a WHO malaria microscopy evaluation slide set","volume":"20","author":"Horning","year":"2021","journal-title":"Malar. J."},{"key":"ref_30","first-page":"91","article-title":"Faster r-cnn: Towards real-time object detection with region proposal networks","volume":"28","author":"Ren","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 11\u201318). Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, faster, stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201329). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Vuola, A.O., Akram, S.U., and Kannala, J. (2019, January 8\u201311). Mask R-CNN and U-Net ensembled for nuclei segmentation. Proceedings of the IEEE International Symposium on Biomedical Imaging, Venice, Italy.","DOI":"10.1109\/ISBI.2019.8759574"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1016\/j.diii.2019.03.002","article-title":"Automatic knee meniscus tear detection and orientation classification with Mask R-CNN","volume":"100","author":"Couteaux","year":"2019","journal-title":"Diagn. Interv. Imaging"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Johnson, J.W. (2019). Automatic nucleus segmentation with Mask R-CNN. Science and Information Conference, Springer.","DOI":"10.1007\/978-3-030-17798-0_32"},{"key":"ref_38","unstructured":"Kopelowitz, E., and Engelhard, G. (2019). Lung Nodules Detection and Segmentation Using 3D Mask R-CNN. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Almubarak, H., Bazi, Y., and Alajlan, N. (2020). Two-stage Mask R-CNN approach for detecting and segmenting the optic nerve head, optic disc, and optic cup in fundus images. Appl. Sci., 10.","DOI":"10.3390\/app10113833"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"85-1","DOI":"10.2352\/ISSN.2470-1173.2020.8.IMAWM-085","article-title":"Small object bird detection in infrared drone videos using Mask R-CNN deep learning","volume":"2020","author":"Kassim","year":"2020","journal-title":"Electron. Imaging"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_43","unstructured":"Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., and Keutzer, K. (2016). SqueezeNet: AlexNet-level accuracy with 50\u00d7 fewer parameters and <0.5 MB model size. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27\u201330). Rethinking the inception architecture for computer vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A. (2017, January 4\u20139). Inception-v4, inception-resnet and the impact of residual connections on learning. Proceedings of the AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"ref_47","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20136). Imagenet classification with deep convolutional neural networks. Proceedings of the Advances in Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1471","DOI":"10.1186\/s12879-020-05453-1","article-title":"Malaria Screener: A smartphone application for automated malaria screening","volume":"20","author":"Yu","year":"2020","journal-title":"BMC Infect. Dis."}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/11\/11\/1994\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:24:43Z","timestamp":1760167483000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/11\/11\/1994"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,27]]},"references-count":48,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["diagnostics11111994"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics11111994","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,27]]}}}