{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T23:37:24Z","timestamp":1773358644319,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2024,7,12]],"date-time":"2024-07-12T00:00:00Z","timestamp":1720742400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Manipal Academy of Higher Education"},{"name":"UGC (India)"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>A digital microscope plays a crucial role in the better and faster diagnosis of an abnormality using various techniques. There has been significant development in this domain of digital pathology. Sickle cell disease (SCD) is a genetic disorder that affects hemoglobin in red blood cells. The traditional method for diagnosing sickle cell disease involves preparing a glass slide and viewing the slide using the eyepiece of a manual microscope. The entire process thus becomes very tedious and time consuming. This paper proposes a semi-automated system that can capture images based on a predefined program. It has an XY stage for moving the slide horizontally or vertically and a Z stage for focus adjustments. The case study taken here is of SCD. The proposed hardware captures SCD slides, which are further used to classify them with respect to normal. They are processed using deep learning models such as Darknet-19, ResNet50, ResNet18, ResNet101, and GoogleNet. The tested models demonstrated strong performance, with most achieving high metrics across different configurations varying with an average of around 97%. In the future, this semi-automated system will benefit pathologists and can be used in rural areas, where pathologists are in short supply.<\/jats:p>","DOI":"10.3390\/info15070403","type":"journal-article","created":{"date-parts":[[2024,7,12]],"date-time":"2024-07-12T08:16:57Z","timestamp":1720772217000},"page":"403","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Explainable Artificial Intelligence and Deep Learning Methods for the Detection of Sickle Cell by Capturing the Digital Images of Blood Smears"],"prefix":"10.3390","volume":"15","author":[{"given":"Neelankit Gautam","family":"Goswami","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Manipal Institute of Technology (MIT), Manipal Academy of Higher Education, Manipal 576104, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3345-360X","authenticated-orcid":false,"given":"Niranjana","family":"Sampathila","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Manipal Institute of Technology (MIT), Manipal Academy of Higher Education, Manipal 576104, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9710-2289","authenticated-orcid":false,"given":"Giliyar Muralidhar","family":"Bairy","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Manipal Institute of Technology (MIT), Manipal Academy of Higher Education, Manipal 576104, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anushree","family":"Goswami","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Manipal Institute of Technology (MIT), Manipal Academy of Higher Education, Manipal 576104, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8298-8224","authenticated-orcid":false,"given":"Dhruva Darshan","family":"Brp Siddarama","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Manipal Institute of Technology (MIT), Manipal Academy of Higher Education, Manipal 576104, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sushma","family":"Belurkar","sequence":"additional","affiliation":[{"name":"Department of Pathology, Kasturba Medical College, Manipal Academy of Higher Education (MAHE), Manipal 576104, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"839","DOI":"10.1093\/oxfordjournals.aje.a010288","article-title":"Sickle hemoglobin (Hb S) allele and sickle cell disease: A HuGE review","volume":"151","author":"Yang","year":"2000","journal-title":"Am. J. Epidemiol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"341","DOI":"10.3390\/hemato3020024","article-title":"Sickle cell disease, a review","volume":"3","author":"Tebbi","year":"2022","journal-title":"Hemato"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"18010","DOI":"10.1038\/nrdp.2018.10","article-title":"Sickle cell disease","volume":"4","author":"Kato","year":"2018","journal-title":"Nat. Rev. Dis. Primers"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Bender, M.A., Hulihan, M., Dorley, M.C., Aguinaga, M.D.P., Ojodu, J., and Yusuf, C. (2021). Newborn screening practices for beta-thalassemia in the United States. Int. J. Neonatal Screen., 7.","DOI":"10.3390\/ijns7040083"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Nkya, S., Mwita, L., Mgaya, J., Kumburu, H., Van Zwetselaar, M., Menzel, S., Mazandu, G.K., Sangeda, R., and Chimusa, E. (2020). Identifying genetic variants and pathways associated with extreme levels of fetal hemoglobin in sickle cell disease in Tanzania. BMC Med. Genet., 21.","DOI":"10.1186\/s12881-020-01059-1"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1126\/science.110.2846.64","article-title":"The inheritance of sickle cell anemia","volume":"110","author":"Neel","year":"1949","journal-title":"Science"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1073","DOI":"10.1177\/0193945910371482","article-title":"Reproductive decisions in people with sickle cell disease or sickle cell trait","volume":"32","author":"Gallo","year":"2010","journal-title":"West. J. Nurs. Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1038\/s41746-020-0282-y","article-title":"Automated screening of sickle cells using a smartphone-based microscope and deep learning","volume":"3","author":"Rivenson","year":"2020","journal-title":"NPJ Digit. Med."},{"key":"ref_9","unstructured":"Data (2020, May 02). Statistics on Sickle Cell Disease, Available online: https:\/\/www.cdc.gov\/sickle-cell\/data\/index.html."},{"key":"ref_10","unstructured":"Shaikh, M., Bhat, N., and Shetty, R. (2014). Automated Red Blood Cells Count. [Ph.D. Dissertation, University of Mumbai]."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Jahn, S.W., Plass, M., and Moinfar, F. (2020). Digital pathology: Advantages, limitations, and emerging perspectives. J. Clin. Med., 9.","DOI":"10.3390\/jcm9113697"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.visinf.2021.07.002","article-title":"Algorithms for rapid digitalization of prescriptions","volume":"5","author":"Gupta","year":"2021","journal-title":"Vis. Inform."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1038\/s41379-021-00929-0","article-title":"Integrating digital pathology into clinical practice","volume":"35","author":"Hanna","year":"2022","journal-title":"Mod. Pathol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1016\/j.prp.2009.05.004","article-title":"Digital slides: Present status of a tool for consultation, teaching, and quality control in pathology","volume":"205","author":"Rocha","year":"2009","journal-title":"Pathol. Res. Pract."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1097\/PAS.0000000000000948","article-title":"Whole slide imaging versus microscopy for primary diagnosis in surgical pathology: A multicenter blinded randomized noninferiority study of 1992 cases (pivotal study)","volume":"42","author":"Mukhopadhyay","year":"2018","journal-title":"Am. J. Surg. Pathol."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"153040","DOI":"10.1016\/j.prp.2020.153040","article-title":"The future of pathology is digital","volume":"216","author":"Pallua","year":"2020","journal-title":"Pathol.-Res. Pract."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Habibi Aghdam, H., Jahani Heravi, E., Habibi Aghdam, H., and Jahani Heravi, E. (2017). Convolutional Neural Networks, Springer.","DOI":"10.1007\/978-3-319-57550-6"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"9","DOI":"10.4103\/jpi.jpi_82_18","article-title":"Introduction to digital image analysis in whole slide imaging: A white paper from the digital pathology association","volume":"10","author":"Aeffner","year":"2019","journal-title":"J. Pathol. Inform."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"16878","DOI":"10.1038\/s41598-017-17204-5","article-title":"QuPath: Open source software for digital pathology image analysis","volume":"7","author":"Bankhead","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"211","DOI":"10.5858\/135.2.211","article-title":"The future of telepathology for the developing world","volume":"135","author":"Hitchcock","year":"2011","journal-title":"Arch. Pathol. Lab. Med."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"51","DOI":"10.4103\/2153-3539.91129","article-title":"Telecytology: Clinical applications, current challenges, and future benefits","volume":"2","author":"Thrall","year":"2011","journal-title":"J. Pathol. Inform."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"510","DOI":"10.1002\/ase.1774","article-title":"The virtual microscopy database\u2014Sharing digital microscope images for research and education","volume":"11","author":"Lee","year":"2018","journal-title":"Anat. Sci. Educ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1002\/ase.2038","article-title":"Virtual microscopy and other technologies for teaching histology during COVID-19","volume":"14","author":"Caruso","year":"2021","journal-title":"Anat. Sci. Educ."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Dey, P., and Dey, P. (2018). Digital image analysis and virtual microscopy in pathology. Basic and Advanced Laboratory Techniques in Histopathology and Cytology, Springer.","DOI":"10.1007\/978-981-10-8252-8"},{"key":"ref_26","unstructured":"Sinha, N., and Ramakrishnan, A. (2003, January 15\u201317). Automation of differential blood count. Proceedings of the TENCON 2003. Conference on Convergent Technologies for Asia-Pacific Region, Bangalore, India."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"615","DOI":"10.3844\/ajassp.2012.615.619","article-title":"Feature extraction and classification of blood cells using artificial neural network","volume":"9","author":"Veluchamy","year":"2012","journal-title":"Am. J. Appl. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1669","DOI":"10.1049\/ipr2.12439","article-title":"An algorithm to detect overlapping red blood cells for sickle cell disease diagnosis","volume":"16","author":"Vicent","year":"2022","journal-title":"IET Image Process."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Bharath, A., Scott, A.W., and Ong, S.S. (2024). Sickle cell retinopathy. Retinal and Choroidal Vascular Diseases of the Eye, Elsevier.","DOI":"10.1016\/B978-0-443-15583-3.00034-2"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2675","DOI":"10.1038\/s41433-021-01556-4","article-title":"Artificial intelligence for improving sickle cell retinopathy diagnosis and management","volume":"35","author":"Cai","year":"2021","journal-title":"Eye"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Dheyab, H.F., Ucan, O.N., Khalaf, M., and Mohammed, A.H. (2020, January 22\u201324). Implementation a various types of machine learning approaches for biomedical datasets based on sickle cell disorder. Proceedings of the 2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), Istanbul, Turkey.","DOI":"10.1109\/ISMSIT50672.2020.9254994"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"200","DOI":"10.28991\/esj-2021-01270","article-title":"Identification of sickle cell anemia using deep neural networks","volume":"5","author":"Yeruva","year":"2021","journal-title":"Emerg. Sci. J."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"15047","DOI":"10.1007\/s11042-017-5088-9","article-title":"Detection of anaemia disease in human red blood cells using cell signature, neural networks, and SVM","volume":"77","author":"Elsalamony","year":"2018","journal-title":"Multimed. Tools Appl."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Patgiri, C., and Ganguly, A. (2021). Adaptive thresholding technique based classification of red blood cell and sickle cell using Na\u00efve Bayes Classifier and Knearest neighbor classifier. Biomed. Signal Process. Control, 68.","DOI":"10.1016\/j.bspc.2021.102745"},{"key":"ref_35","unstructured":"Alzubaidi, L., AlShamma, O., Fadhel, M.A., Farhan, L., and Zhang, J. (2018, January 6\u20138). Classification of red blood cells in sickle cell anemia using deep convolutional neural network. Proceedings of the Intelligent Systems Design and Applications: 18th International Conference on Intelligent Systems Design and Applications (ISDA 2018), Vellore, India."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"102470","DOI":"10.1016\/j.media.2022.102470","article-title":"Explainable artificial intelligence (XAI) in deep learningbased medical image analysis","volume":"79","author":"Kuijf","year":"2022","journal-title":"Med. Image Anal."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Xu, M., Papageorgiou, D.P., Abidi, S.Z., Dao, M., Zhao, H., and Karniadakis, G.E. (2017). A deep convolutional neural network for classification of red blood cells in sickle cell anemia. PLoS Comput. Biol., 13.","DOI":"10.1371\/journal.pcbi.1005746"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Gedefaw, L., Liu, C.-F., Ip, R.K.L., Tse, H.-F., Yeung, M.H.Y., Yip, S.P., and Huang, C.-L. (2023). Artificial intelligence-assisted diagnostic cytology and genomic testing for hematologic disorders. Cells, 12.","DOI":"10.3390\/cells12131755"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.cca.2019.10.025","article-title":"Emerging point-of-care technologies for sickle cell disease diagnostics","volume":"501","author":"Ilyas","year":"2020","journal-title":"Clin. Chim. Acta"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Butt, M., and de Keijzer, A. (2022). Using Transfer Learning to Train a Binary Classifier for Lorrca Ektacytometery Microscopic Images of Sickle Cells and Healthy Red Blood Cells. Data, 7.","DOI":"10.3390\/data7090126"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Alzubaidi, L., Fadhel, M.A., AlShamma, O., Zhang, J., and Duan, Y. (2020). Deep learning models for classification of red blood cells in microscopy images to aid in sickle cell anemia diagnosis. Electronics, 9.","DOI":"10.3390\/electronics9030427"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1111\/myc.13209","article-title":"The design and application of an automated microscope developed based on deep learning for fungal detection in dermatology","volume":"64","author":"Gao","year":"2021","journal-title":"Mycoses"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"7223","DOI":"10.1364\/BOE.439014","article-title":"MicroHikari3D: An automated DIY digital microscopy platform with deep learning capabilities","volume":"12","author":"Salido","year":"2021","journal-title":"Biomed. Opt. Express"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/15\/7\/403\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:15:33Z","timestamp":1760109333000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/15\/7\/403"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,12]]},"references-count":43,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["info15070403"],"URL":"https:\/\/doi.org\/10.3390\/info15070403","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,12]]}}}