{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,26]],"date-time":"2026-07-26T07:23:40Z","timestamp":1785050620439,"version":"3.55.0"},"reference-count":61,"publisher":"PeerJ","license":[{"start":{"date-parts":[[2021,4,21]],"date-time":"2021-04-21T00:00:00Z","timestamp":1618963200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Any contamination in the human body can prompt changes in blood cell morphology and various parameters of cells. The minuscule images of blood cells are examined for recognizing the contamination inside the body with an expectation of maladies and variations from the norm. Appropriate segmentation of these cells makes the detection of a disease progressively exact and vigorous. Microscopic blood cell analysis is a critical activity in the pathological analysis. It highlights the investigation of appropriate malady after exact location followed by an order of abnormalities, which assumes an essential job in the analysis of various disorders, treatment arranging, and assessment of results of treatment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methodology<\/jats:title>\n                    <jats:p>A survey of different areas where microscopic imaging of blood cells is used for disease detection is done in this paper. Research papers from this area are obtained from a popular search engine, Google Scholar. The articles are searched considering the basics of blood such as its composition followed by staining of blood, that is most important and mandatory before microscopic analysis. Different methods for classification, segmentation of blood cells are reviewed. Microscopic analysis using image processing, computer vision and machine learning are the main focus of the analysis and the review here. Methodologies employed by different researchers for blood cells analysis in terms of these mentioned algorithms is the key point of review considered in the study.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Different methodologies used for microscopic analysis of blood cells are analyzed and are compared according to different performance measures. From the extensive review the conclusion is made.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>There are different machine learning and deep learning algorithms employed by researchers for segmentation of blood cell components and disease detection considering microscopic analysis. There is a scope of improvement in terms of different performance evaluation parameters. Different bio-inspired optimization algorithms can be used for improvement. Explainable AI can analyze the features of AI implemented system and will make the system more trusted and commercially suitable.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.7717\/peerj-cs.460","type":"journal-article","created":{"date-parts":[[2021,4,21]],"date-time":"2021-04-21T05:37:56Z","timestamp":1618983476000},"page":"e460","source":"Crossref","is-referenced-by-count":55,"title":["A review of microscopic analysis of blood cells for disease detection with AI perspective"],"prefix":"10.7717","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7264-1729","authenticated-orcid":true,"given":"Nilkanth Mukund","family":"Deshpande","sequence":"first","affiliation":[{"name":"Department of Electronics and Telecommunication, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, Maharashtra, India"},{"name":"Electronics & Telecommunication Department, Dr. Vithalrao Vikhe Patil College of Engineering, Ahmednagar, Ahmednagar, Maharashtra, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shilpa","family":"Gite","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Symbiosis Institute of Technology, Pune Symbiosis International (Deemed University), Pune, Maharashtra, India"},{"name":"Symbiosis Center for Applied Artificial Intelligence (SCAAI), Symbiosis International (Deemed University), Pune, Maharashtra, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8508-6066","authenticated-orcid":true,"given":"Rajanikanth","family":"Aluvalu","sequence":"additional","affiliation":[{"name":"Department of CSE, Vardhaman College of Engineering, Hyderabad, Telangana, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"4443","published-online":{"date-parts":[[2021,4,21]]},"reference":[{"key":"10.7717\/peerj-cs.460\/ref-1","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.dib.2015.08.006","article-title":"Microscopic images dataset for automation of RBCs counting","volume":"5","author":"Abbas","year":"2015","journal-title":"Data in Brief"},{"issue":"1","key":"10.7717\/peerj-cs.460\/ref-2","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1049\/iet-ipr.2018.5910","article-title":"Automatic detection of acute lymphoblastic leukaemia based on extending the multifractal features","volume":"14","author":"Abbasi","year":"2019","journal-title":"IET Image Processing"},{"issue":"8","key":"10.7717\/peerj-cs.460\/ref-3","doi-asserted-by":"publisher","first-page":"1783","DOI":"10.1007\/s11517-019-01984-1","article-title":"Detection of acute lymphoblastic leukemia using image segmentation and data mining algorithms","volume":"57","author":"Acharya","year":"2019","journal-title":"Medical and Biological Engineering and Computing"},{"issue":"3","key":"10.7717\/peerj-cs.460\/ref-4","doi-asserted-by":"publisher","first-page":"995","DOI":"10.1109\/JSYST.2014.2308452","article-title":"Automated screening system for acute myelogenous leukemia detection in blood microscopic images","volume":"8","author":"Agaian","year":"2014","journal-title":"IEEE Systems Journal"},{"issue":"3","key":"10.7717\/peerj-cs.460\/ref-5","doi-asserted-by":"publisher","first-page":"104","DOI":"10.3390\/diagnostics9030104","article-title":"Identification of leukemia subtypes from microscopic images using convolutional neural network","volume":"9","author":"Ahmed","year":"2019","journal-title":"Diagnostics"},{"issue":"2","key":"10.7717\/peerj-cs.460\/ref-6","doi-asserted-by":"publisher","first-page":"e7702","DOI":"10.7717\/peerj.7702","article-title":"The impact of artificial intelligence in medicine on the future role of the physician","volume":"7","author":"Ahuja","year":"2019","journal-title":"PeerJ"},{"key":"10.7717\/peerj-cs.460\/ref-7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00521-020-05101-4","article-title":"Gene encoder: a feature selection technique through unsupervised deep learning-based clustering for large gene expression data","author":"Al-Obeidat","year":"2020","journal-title":"Neural Computing and Applications"},{"issue":"13","key":"10.7717\/peerj-cs.460\/ref-8","doi-asserted-by":"publisher","first-page":"2595","DOI":"10.1049\/iet-ipr.2018.6515","article-title":"Improved image segmentation algorithms for detecting types of acute lymphatic leukaemia","volume":"13","author":"Al-Tahhan","year":"2019","journal-title":"IET Image Processing"},{"key":"10.7717\/peerj-cs.460\/ref-9","first-page":"222","article-title":"Microscopic blood cell classification using inception recurrent residual convolutional neural networks","author":"Alom","year":"2018"},{"key":"10.7717\/peerj-cs.460\/ref-10","article-title":"Sickle blood cell detection based on image segmentation. 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