{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,12]],"date-time":"2026-11-12T22:28:41Z","timestamp":1794522521677,"version":"4.9.1"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-025-20402-1","type":"journal-article","created":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T09:14:11Z","timestamp":1761038051000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["DANet a lightweight dilated attention network for malaria parasite detection"],"prefix":"10.1038","volume":"15","author":[{"given":"Asfak","family":"Ali","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rajdeep","family":"Pal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ishandeep","family":"Dey","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Erik","family":"Cuevas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marco","family":"Perez-Cisneros","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ram","family":"Sarkar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,10,21]]},"reference":[{"key":"20402_CR1","unstructured":"World Health Organization. Malaria, 2024. Accessed: date-of-access."},{"issue":"4","key":"20402_CR2","first-page":"2138","volume":"8","author":"MM Salih","year":"2020","unstructured":"Salih, M. M., Abdulaali, A. R. & Falih, H. N. A proposed lightweight image encryption using chacha with hyperchaotic maps. Period Eng Nat Sci 8(4), 2138\u20132145 (2020).","journal-title":"Period Eng Nat Sci"},{"issue":"1","key":"20402_CR3","doi-asserted-by":"publisher","first-page":"5052","DOI":"10.1038\/srep05052","volume":"4","author":"HC Maximilian","year":"2014","unstructured":"Maximilian, H. C., Mengmeng, F. L. H. & Bekir, \u00dc. PCR amplification of repetitive DNA: A limitation to genome editing technologies and many other applications. Sci Rep 4(1), 5052 (2014).","journal-title":"Sci Rep"},{"key":"20402_CR4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12936-018-2635-4","volume":"18","author":"MO Kenji","year":"2019","unstructured":"Kenji, M. O. et al. A comparison of thick-film microscopy, rapid diagnostic test, and polymerase chain reaction for accurate diagnosis of plasmodium falciparum malaria. Malaria J 18, 1\u20138 (2019).","journal-title":"Malaria J"},{"key":"20402_CR5","doi-asserted-by":"crossref","unstructured":"Vishnu, M., Yuhang, D., David, P. W. A comparison of feature selection methods for machine learning based automatic malarial cell recognition in wholeslide images. In 2016 IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), pages 216\u2013219 (2016).","DOI":"10.1109\/BHI.2016.7455873"},{"issue":"3","key":"20402_CR6","first-page":"1829","volume":"8","author":"Hayder Najm","year":"2020","unstructured":"Najm, Hayder, Hoomod, Haider K. & Hassan, Rehab. A proposed hybrid cryptography algorithm based on gost and salsa (20). Period Eng Nat Sci 8(3), 1829\u20131835 (2020).","journal-title":"Period Eng Nat Sci"},{"key":"20402_CR7","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1146\/annurev-bioeng-071516-044442","volume":"19","author":"S Dinggang","year":"2017","unstructured":"Dinggang, S., Guorong, W. & Heung-Il, S. Deep learning in medical image analysis. Ann Rev Biomed Eng 19, 221\u2013248 (2017).","journal-title":"Ann Rev Biomed Eng"},{"key":"20402_CR8","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1007\/s12539-020-00367-7","volume":"12","author":"Quan Quan","year":"2020","unstructured":"Quan, Quan, Wang, Jianxin & Liu, Liangliang. An effective convolutional neural network for classifying red blood cells in malaria diseases. Interdiscip Sci: Comput Life Sci 12, 217\u2013225 (2020).","journal-title":"Interdiscip Sci: Comput Life Sci"},{"key":"20402_CR9","doi-asserted-by":"crossref","unstructured":"Xian, Z., Oubo, G. Wenxin, H., Jingling, Y. Bo, M., Ryan Wen, L. Multi-scale residual network for image classification. In ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2023\u20132027 (2020).","DOI":"10.1109\/ICASSP40776.2020.9053478"},{"key":"20402_CR10","doi-asserted-by":"publisher","first-page":"9099","DOI":"10.1109\/ACCESS.2017.2705642","volume":"5","author":"Dhanya Bibin","year":"2017","unstructured":"Bibin, Dhanya, Nair, Madhu S. & Punitha, P. Malaria parasite detection from peripheral blood smear images using deep belief networks. IEEE Access 5, 9099\u20139108 (2017).","journal-title":"IEEE Access"},{"issue":"11","key":"20402_CR11","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y Lecun","year":"1998","unstructured":"Lecun, Y., Bottou, L., Bengio, Y. & Haffner, P. Gradient-based learning applied to document recognition. Proc IEEE 86(11), 2278\u20132324 (1998).","journal-title":"Proc IEEE"},{"issue":"6","key":"20402_CR12","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1145\/3065386","volume":"60","author":"Alex Krizhevsky","year":"2017","unstructured":"Krizhevsky, Alex, Sutskever, Ilya & Hinton, Geoffrey E. Imagenet classification with deep convolutional neural networks. Commun. ACM 60(6), 84\u201390 (2017).","journal-title":"Commun. ACM"},{"key":"20402_CR13","doi-asserted-by":"crossref","unstructured":"Christian, S., Wei, L., Yangqing, J., Pierre, S., Scott, Dragomir, A., Dumitru, E., Vincent, V., Andrew, R. Going deeper with convolutions. In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 1\u20139 (2015).","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"20402_CR14","first-page":"1","volume":"9","author":"DS Shuleenda","year":"2016","unstructured":"Shuleenda, D. S., Shah, S., Anuradha, T. & Rabul, L. Malaria infected erythrocyte classification based on the histogram features using microscopic images of thin blood smear. Indian J Sci Technol 9, 1\u201310 (2016).","journal-title":"Indian J Sci Technol"},{"key":"20402_CR15","first-page":"24","volume-title":"Information and communication technology for development for Africa","author":"GY Girmay","year":"2018","unstructured":"Girmay, G. Y., Hagos, G. M. Y., Adugna, E. E. & Niguse, G. T. G. Malaria detection and classification using machine learning algorithms. In Information and communication technology for development for Africa (eds Mekuria, F. et al.) 24\u201333 (Springer International Publishing, Cham, 2018)."},{"key":"20402_CR16","unstructured":"Zazilah, M., Aziz, M., Azreen, S. S., Rabi\u2019ahtuladawiah, S. Automated quantification and classification of malaria parasites in thin blood smears. In 2013 IEEE International Conference on Signal and Image Processing Applications, pp. 369\u2013373 (2013)."},{"key":"20402_CR17","doi-asserted-by":"crossref","unstructured":"Leila, M., Karim, A.-A., Abdolamir, B. Malaria parasite detection in giemsa-stained blood cell images. 2013 8th Iranian Conference on Machine Vision and Image Processing (MVIP), 360\u2013365 (2013).","DOI":"10.1109\/IranianMVIP.2013.6780011"},{"key":"20402_CR18","doi-asserted-by":"crossref","unstructured":"Mandal, S., Kumar, A., Chatterjee, J., Manjunatha, M., Ray, A.K. Segmentation of blood smear images using normalized cuts for detection of malarial parasites. 2010 Annual IEEE India Conference (INDICON), 1\u20134 (2010).","DOI":"10.1109\/INDCON.2010.5712739"},{"key":"20402_CR19","doi-asserted-by":"crossref","unstructured":"Anggraini, D., Nugroho, A.S., Pratama, C., Rozi, I.E., Iskandar, A.A., Hartono, R.N. Automated status identification of microscopic images obtained from malaria thin blood smears using bayes decision: A study case in plasmodium falciparum. 2011 International Conference on Advanced Computer Science and Information Systems, 347\u2013352 (2011).","DOI":"10.1109\/ICEEI.2011.6021762"},{"key":"20402_CR20","doi-asserted-by":"crossref","unstructured":"Charpe, K., Bairagi, V.K., Desarda, S., Barshikar, S. A novel method for automatic detection of malaria parasite stage in microscopic blood image. International Journal of Computer Applications, 128(17), (2015).","DOI":"10.5120\/ijca2015906763"},{"key":"20402_CR21","first-page":"431","volume-title":"Global trends in information systems and software applications Springer","author":"J Somasekar","year":"2012","unstructured":"Somasekar, J., Rama Mohan Reddy, A. & Sreenivasulu Reddy, L. An efficient algorithm for automatic malaria detection in microscopic blood images. In Global trends in information systems and software applications Springer (eds Venkata, P. et al.) 431\u2013440 (Berlin Heidelberg, Berlin, 2012)."},{"key":"20402_CR22","doi-asserted-by":"publisher","first-page":"15297","DOI":"10.1007\/s11042-019-7162-y","volume":"79","author":"A Vijayalakshmi","year":"2020","unstructured":"Vijayalakshmi, A. & Rajesh Kanna, B. Deep learning approach to detect malaria from microscopic images. Multimedia Tools Appl. 79, 15297\u201315317 (2020).","journal-title":"Multimedia Tools Appl."},{"issue":"5","key":"20402_CR23","doi-asserted-by":"publisher","first-page":"1700","DOI":"10.1016\/j.jksuci.2020.07.003","volume":"34","author":"PA Pattanaik","year":"2022","unstructured":"Pattanaik, P. A., Mittal, Mohit, Khan, Mohammad Zubair & Panda, S. N. Malaria detection using deep residual networks with mobile microscopy. J King Saud Univ- Comput Inf Sci 34(5), 1700\u20131705 (2022).","journal-title":"J King Saud Univ- Comput Inf Sci"},{"issue":"7","key":"20402_CR24","doi-asserted-by":"publisher","DOI":"10.1111\/exsy.12695","volume":"39","author":"Aayush Kumar","year":"2022","unstructured":"Kumar, Aayush, Singh, Sanat B., Satapathy, Suresh Chandra & Rout, Minakhi. Mosquito-net: A deep learning based cadx system for malaria diagnosis along with model interpretation using gradcam and class activation maps. Expert Syst 39(7), e12695 (2022).","journal-title":"Expert Syst"},{"issue":"8","key":"20402_CR25","doi-asserted-by":"publisher","first-page":"1779","DOI":"10.1080\/0952813X.2023.2165724","volume":"36","author":"Asifullah Khan","year":"2023","unstructured":"Khan, Asifullah et al. A survey of deep learning techniques for the analysis of covid-19 and their usability for detecting omicron. J Exp Theor Artif Intell 36(8), 1779\u20131821 (2023).","journal-title":"J Exp Theor Artif Intell"},{"key":"20402_CR26","first-page":"147","volume":"15","author":"Francis Jesmar Montalbo","year":"2021","unstructured":"Montalbo, Francis Jesmar & Alon, Alvin. Empirical analysis of a fine-tuned deep convolutional model in classifying and detecting malaria parasites from blood smears. KSII Trans Internet Inf Syst 15, 147\u2013165 (2021).","journal-title":"KSII Trans Internet Inf Syst"},{"issue":"5","key":"20402_CR27","doi-asserted-by":"publisher","first-page":"1427","DOI":"10.1109\/JBHI.2019.2939121","volume":"24","author":"Feng Yang","year":"2020","unstructured":"Yang, Feng et al. Deep learning for smartphone-based malaria parasite detection in thick blood smears. IEEE J Biomed Health Inf 24(5), 1427\u20131438 (2020).","journal-title":"IEEE J Biomed Health Inf"},{"issue":"2","key":"20402_CR28","first-page":"175","volume":"15","author":"Kaustubh Chakradeo","year":"2021","unstructured":"Chakradeo, Kaustubh, Delves, Michael & Titarenko, Sofya. Malaria parasite detection using deep learning methods. Int J Comput Inf Eng 15(2), 175\u2013182 (2021).","journal-title":"Int J Comput Inf Eng"},{"issue":"1","key":"20402_CR29","doi-asserted-by":"publisher","first-page":"13249","DOI":"10.1038\/s41598-024-63831-0","volume":"14","author":"M Muhammad","year":"2024","unstructured":"Muhammad, M. et al. Efficient deep learning-based approach for malaria detection using red blood cell smears. Sci Rep 14(1), 13249 (2024).","journal-title":"Sci Rep"},{"key":"20402_CR30","doi-asserted-by":"crossref","unstructured":"Qayyum, A.B., Islam, T., Haque, M.A. Malaria diagnosis with dilated convolutional neural network based image analysis. In 2019 IEEE International Conference on Biomedical Engineering, Computer and Information Technology for Health (BECITHCON), 68\u201372 (2019).","DOI":"10.1109\/BECITHCON48839.2019.9063179"},{"key":"20402_CR31","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K. Q.. Densely connected convolutional networks. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2261\u20132269 (2017).","DOI":"10.1109\/CVPR.2017.243"},{"key":"20402_CR32","unstructured":"Yunpeng, C., Jianan, L., Huaxin, X., Xiaojie, J., Shuicheng, Y., Jiashi, F. Dual path networks. Advances in neural information processing systems, 30 (2017)."},{"issue":"10","key":"20402_CR33","doi-asserted-by":"publisher","first-page":"13237","DOI":"10.1007\/s11042-021-10946-5","volume":"81","author":"T Banerjee","year":"2022","unstructured":"Banerjee, T. et al. Deep convolutional neural network (falcon) and transfer learning-based approach to detect malarial parasite. Multimed Tools Appl 81(10), 13237\u201313251 (2022).","journal-title":"Multimed Tools Appl"},{"issue":"23","key":"20402_CR34","doi-asserted-by":"publisher","first-page":"63593","DOI":"10.1007\/s11042-023-17866-6","volume":"83","author":"A Murmu","year":"2024","unstructured":"Murmu, A. & Kumar, P. Dlrfnet: Deep learning with random forest network for classification and detection of malaria parasite in blood smear. Multimed Tools Appl 83(23), 63593\u201363615 (2024).","journal-title":"Multimed Tools Appl"},{"key":"20402_CR35","doi-asserted-by":"publisher","first-page":"71705","DOI":"10.1109\/ACCESS.2024.3402442","volume":"12","author":"A Dev","year":"2024","unstructured":"Dev, A., Fouda, M. M., Kerby, L. & Fadlullah, Z. M. Advancing malaria identification from microscopic blood smears using hybrid deep learning frameworks. IEEE Access 12, 71705\u201371715 (2024).","journal-title":"IEEE Access"},{"key":"20402_CR36","unstructured":"Aanjan, K.S., Kumar, M.M., Kumar, P.S., Dhanaraj, L.S., Kumar, R., Karthikeyan, P. Application of hybrid capsule network model for malaria parasite detection on microscopic blood smear images. Multimedia Tools and Applications, 1\u201327 (2024)."},{"key":"20402_CR37","doi-asserted-by":"crossref","unstructured":"Prema, A.T., Santosh, P., Sakshi, G., Divya, B., Bhagyashree, G. Classification of blood smear images using CNN and pretrained vgg16: Computer aided diagnosis of malaria disease. In 2024 First International Conference on Technological Innovations and Advance Computing (TIACOMP), pages 349\u2013354. IEEE, (2024).","DOI":"10.1109\/TIACOMP64125.2024.00065"},{"key":"20402_CR38","doi-asserted-by":"crossref","unstructured":"Kumar, A., Nelson, L., Rasher, S. and Surendran, R.: Mosquitonet based deep learning approach for malaria parasite detection using cell images. In 2024 International Conference on Automation and Computation (AUTOCOM), pages 164\u2013169. IEEE, (2024).","DOI":"10.1109\/AUTOCOM60220.2024.10486136"},{"key":"20402_CR39","doi-asserted-by":"crossref","unstructured":"Manish, M., Singh, K.A., Ankit, B., Abhishek, B., Eshika, J., et al. Automated malaria detection using deep learning: A convolutional neural network approach for microscopic cell image analysis. In 2024 International Conference on Decision Aid Sciences and Applications (DASA), pages 1\u20136. IEEE, (2024).","DOI":"10.1109\/DASA63652.2024.10836383"},{"key":"20402_CR40","doi-asserted-by":"crossref","unstructured":"Retinderdeep, S., Neha, S., Priyanshi, A., Mukesh, S., Ravi, C.K. Revolutionary changes in acute lymphoblastic leukaemia classification: The impact of deep learning convolutional neural networks. In 2024 2nd International Conference on Computer, Communication and Control (IC4), pages 1\u20136. IEEE, (2024).","DOI":"10.1109\/IC457434.2024.10486672"},{"key":"20402_CR41","doi-asserted-by":"crossref","unstructured":"Gokulkrishnan, N., Nayak, T. and Sampathila, N.. Deep learning-based analysis of blood smear images for detection of acute lymphoblastic leukemia. In 2023 IEEE international conference on electronics, computing and communication technologies (CONECCT), pages 1\u20135. IEEE, (2023).","DOI":"10.1109\/CONECCT57959.2023.10234824"},{"key":"20402_CR42","unstructured":"Tusar, M.T.H.K. and Anik, R.K.. Automated detection of acute lymphoblastic leukemia subtypes from microscopic blood smear images using deep neural networks. arXiv preprint arXiv:2208.08992, (2022)."},{"key":"20402_CR43","doi-asserted-by":"crossref","unstructured":"Muhamad, B.A., Esa, P.B., Teguh, S.R. Acute lymphoblastic leukemia image classification performance with transfer learning using cnn architecture. In 2022 4th international conference on biomedical engineering (IBIOMED), pages 30\u201335. IEEE, (2022).","DOI":"10.1109\/IBIOMED56408.2022.9988690"},{"key":"20402_CR44","doi-asserted-by":"crossref","unstructured":"Manohar, N., Raj, V.N.P., Tejaswini, N.N., Phanivarma, M., Jahnavi, G. Acute lymphoblastic leukemia classification with jetson nano. In 2024 2nd International Conference on Device Intelligence, Computing and Communication Technologies (DICCT), pages 249\u2013253. IEEE, (2024).","DOI":"10.1109\/DICCT61038.2024.10532966"},{"key":"20402_CR45","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J. and Wojna, Z. Rethinking the inception architecture for computer vision. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2818\u20132826 (2016).","DOI":"10.1109\/CVPR.2016.308"},{"key":"20402_CR46","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S. and Sun, J. Deep residual learning for image recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770\u2013778 (2016).","DOI":"10.1109\/CVPR.2016.90"},{"key":"20402_CR47","volume-title":"Advances in neural information processing systems","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky, A., Sutskever, I. & Hinton, G. E. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems Vol. 25 (eds Pereira, F. et al.) (Curran Associates Inc, Red Hook, 2012)."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-20402-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-20402-1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-20402-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T09:14:33Z","timestamp":1761038073000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-20402-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"references-count":47,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["20402"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-20402-1","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]},"assertion":[{"value":"26 April 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 October 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"The authors declare that they have no conflict of interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any of the authors. No clinical validation involving human participants was conducted in this study; however, future work will aim to include clinical validation in collaboration with medical professionals to assess real-world performance.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}}],"article-number":"36689"}}