Peer Review #2 of "A review of microscopic analysis of blood cells for disease detection with AI perspective (v0.1)"
2021
Background 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 examination.It highlights the investigation of appropriate malady after exact location followed by order of abnormalities, which assumes an essential job in analyzing various disorders, treatment arranging, and assessment of results of treatment.Methodology 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, which is the most essential and mandatory before microscopic analysis.Various methods for classification, segmentation of blood cells are reviewed.Microscopic analysis using image processing, computer vision, and machine learning is the main focus of the research and review.Methodologies employed by various researchers for blood cells analysis in terms of these mentioned algorithms is the critical point of review considered in the study.Results Different methodologies used for microscopic analysis of blood cells are analyzed and are compared according to various performance measures.From the extensive review, the conclusion is made.Conclusion Researchers have employed various machine learning and deep learning algorithms for the segmentation of blood cell components and disease detection considering microscopic analysis.There is a scope of improvement in terms of various performance evaluation parameters.Researchers can use different bio-inspired optimization algorithms for performance improvement.Explainable AI can analyze AI system features and make the system more trusted and commercially suitable.