Expert bioinformation system for diagnosing forms of acute leukemia based on analysis of biomedical information
Li Jingiong, Sergii PavlovThe introductory chapter established the context for this paper by stressing the significance of leukemia in healthcare and the challenges associated with both diagnosis and therapy. The paper ultimate objective is to provide an information technology solution to these issues, thereby improving patient care and prognosis. A conceptual model of an expert system for the diagnosis of acute leukemia is proposed, which will reduce the ambiguity in the interpretation of research objects. Factors influencing the correct recognition of complex objects (images of blast and non-blast blood cells) using an expert system based on computer microscopy methods are considered
References
[1] Abdeldaim, A.M., Sahlol, A.T., Elhoseny, M., & Hassanien, A.E. (2018). Computer-aided acute lymphoblastic leukemia diagnosis system based on image analysis. In A. Hassanien & D. Oliva (Eds.), Advances in soft computing and machine learning in image processing. Studies in computational intelligence (Vol. 730, pp 131-147). Cham: Springer. doi: 10.1007/978-3-319-63754-9_7 131-147.
[2] Ahmed, I.A., Senan, E.M., Shatnawi, H.S.A., Alkhraisha, Z.M., & Al-Azzam, M.M.A. (2023). Hybrid techniques for the diagnosis of acute lymphoblastic leukemia based on fusion of CNN features. Diagnostics, 13(6), article number 1026.
[3] Ansari, S., Navin, A.H., Sangar, A.B., Gharamaleki, J.V., & Danishvar, S. (2023). A customized efficient deep learning model for the diagnosis of acute leukemia cells based on lymphocyte and monocyte images. Electronics, 12(2), article number 322.
[4] Arber, D.A., Borowitz, M.J., Cessna, M., Etzell, J., Foucar, K., Hasserjian, R.P., Rizzo, J.D., Theil, K., Wang, S.A., Smith, A.T., Rumble, R.B., Thomas, N.E., & Vardiman, J.W. (2017). Initial diagnostic workup of acute leukemia: Guideline from the College of American Pathologists and the American Society of Hematology. Archives of Pathology & Laboratory Medicine, 141(10), 1342-1393.
[5] Begum, S., Sarkar, R., Chakraborty, D., & Maulik, U. (2020). Identification of biomarker on biological and gene expression data using fuzzy preference based rough set. Journal of Intelligent Systems, 30(1), 130-141.
[6] Grimwade, L.F., Fuller, K.A., & Erber, W.N. (2017). Applications of imaging flow cytometry in the diagnostic assessment of acute leukaemia. Methods, 112, 39-45.
[7] Kadia, T.M. et al. (2016). TP53 mutations in newly diagnosed acute myeloid leukemia: Clinicomolecular characteristics, response to therapy, and outcomes. Cancer, 122(22), 3484-3491.
[8] Rehman, A., Abbas, N., Saba, T., Rahman, S.I.U., Mehmood, Z., & Kolivand, H. (2018). Classification of acute lymphoblastic leukemia using deep learning. Microscopy Research and Technique, 81(11), 1310-1317.
[9] Rose-Inman, H., & Kuehl, D. (2017). Acute leukemia. Hematol Oncol Clin North Am, 31(6), 1011-1028.
[10] Shah, A., Naqvi, S.S., Naveed, K., Salem, N., Khan, M.A., & Alimgeer, K.S. (2021). Automated diagnosis of leukemia: a comprehensive review. IEEE Access, 9, 132097-132124.
[11] Singha, A., Thakur, R.S., & Patel, T. (2021). Deep learning applications in medical image analysis. In S. Dash, S. Kumar Pani, S. Balamurugan & A. Abraham (Eds.), Biomedical data mining for information retrieval: Methodologies, techniques and applications (pp. 293-350). Hoboken: John Wiley & Son.
[12] Vosberg, S., & Greif, P.A. (2019). Clonal evolution of acute myeloid leukemia from diagnosis to relapse. Genes, Chromosomes And Cancer, 58(12), 839-849.
[13] Williams, P., et al. (2019). The distribution of T‐cell subsets and the expression of immune checkpoint receptors and ligands in patients with newly diagnosed and relapsed acute myeloid leukemia. Cancer, 125(9), 1470-1481.
[14] Zolfaghari, M., & Sajedi, H. (2022). A survey on automated detection and classification of acute leukemia and WBCs in microscopic blood cells. Multimedia Tools and Applications, 81(5), 6723-6753.
[15] Timchenko, L., Wojcik, W., Kokriatskaia, N., Levchenko, O., & Kryvinska, N. (2020). New methods of network modelling using parallel-hierarchical networks for processing data and reducing erroneous calculation risk. In International workshop on computational & information technologies for risk-informed systems (Vol 2805, pp. 201-212). Kherson: CEUR-WS.
[16] Timchenko, L.I. (2000). Multi-stage parallel-hierarchical network as a model of a neural-like computing scheme. Cybernetics and System Analysis, 2, 114-134.
[17] Metlitsky, E.A., & Kaverznev, V.V. (1989). Parallel memory systems: Theory, design, application. Leningrad: University Press.
[18] Tymchenko, L., Kokriatska, N., Tverdomed, V., Sachaniuk-Kavets’ka, N., Semenova, L., Riabokon, O., Gromaszek, K., & Mussabekova, A. (2022). Pyramidal method of generalized spatially connected processing and an example of its implementation in image processing. In Proceeding SPIE Photonics applications in astronomy, communications, industry, and high energy physics experiments (Vol. 12476, article number 124760E). Lublin: SPIE. doi: 10.1117/12.2659202.
[19] Wójcik, W., & Smolarz, A. (2017). Information technology in medical diagnostics. Florida: CRC Press.
[20] Wójcik, W., Pavlov, S., & Kalimoldayev, M. (2019). Information technology in medical diagnostics II. London: Taylor & Francis Grou.
[21] Wójcik, W., Pavlov, S. (Eds.). (2022). Highly linear microelectronic sensors signal converters based on push-pull amplifier circuits. Lublin: Comitet Inzynierii Srodowiska PAN.
[22] Nosova, Ya., Pavlov, S., Avrunin, O., Hrushko, O., & Shushlyapina, N. (2021). System of three-dimensional human face images formation for plastic and reconstructive medicine. In P. Arras & D. Luengo (Eds.), Teaching and subjects on bio-medical engineering approaches and experiences from the BIOART-project (pp. 187-203). Leuven: Acco cv.
[23] Romanyuk, O.N., Pavlov, S.V., Dovhaliuk, R.Yu., Babyuk, N.P., Obidnyk, M.D., Kisala, P., & Suleimenov, B. (2013). Microfacet distribution function for physically based bidirectional reflectance distribution functions. In Optical fibers and their applications 2012 (Vol. 8698, pp.132-135). Lublin: SPIE.