Review paper on Artificial intelligence assisted diagnosis for blood cancer using machine learning
Keywords:
Blood cancer, Machine Learning, Deep learning, Medicine 5.0 Technology, Clinical decision making, Artificial intelligenceAbstract
This article guides a review platform that allows the evaluation of artificial intelligence-assisted diagnosis for blood cancer using machine learning. Advanced medical and technology-based research has fuelled the adoption of the latest technologies for the sake of advancement in medical science application and overall improvement in the detection, diagnosis, prevention and treatment of diseases. AI technology is being used widely in medicine, the economy and daily life; in medicine, artificial intelligence is used mainly for treatment, diagnosis and prediction of disease prognosis. This review effectively highlights the wide-ranging applications of AI in medicine, with a specific focus on its contribution to treatment, diagnosis, prognosis and prediction.
References
Abdulqader, D.M., Abdulazeez, A.M. and Zeebaree, D.Q., 2020. Machine learning supervised algorithms of gene selection: A review. Machine Learning, 62(03), pp.233-244.
Ahmed, N., Yigit, A., Isik, Z. and Alpkocak, A., 2019. Identification of leukemia subtypes from microscopic images using convolutional neural network. Diagnostics, 9(3), p.104.
Akazawa, M. and Hashimoto, K., 2020. Artificial intelligence in ovarian cancer diagnosis. Anticancer research, 40(8), pp.4795-4800.
Alsalem, M.A., Zaidan, A.A., Zaidan, B.B., Hashim, M., Albahri, O.S., Albahri, A.S., Hadi, A. and Mohammed, K.I., 2018. Systematic review of an automated multiclass detection and classification system for acute Leukaemia in terms of evaluation and benchmarking, open challenges, issues and methodological aspects. Journal of medical systems, 42(11), pp.1-36.
Barhoom, A.M., 2019. Blood donation prediction using artificial neural network.
Chang, Y.J., Hung, K.C., Wang, L.K., Yu, C.H., Chen, C.K., Tay, H.T., Wang, J.J. and Liu, C.F., 2021. A real-time artificial intelligence-assisted system to predict weaning from ventilator immediately after lung resection surgery. International journal of environmental research and public health, 18(5), p.2713.
Chaurasiya, S. and Rajak, R., 2022. Comparative Analysis of Machine Learning Algorithms in Breast Cancer Classification.
Eckardt, J.N., Rollig, C., Kramer, M., Stasik, S., Georgi, J.A., Heisig, P., Kroschinsky, F.P., Schetelig, J., Platzbecker, U., Müller-Tidow, C. and Sauer, T., 2021. Prediction of complete remission and survival in acute myeloid leukemia using supervised machine learning. Blood, 138, p.108.
Feltes,B.C.,Chandelier, E.B.,Grisci,B.I.andDorn, M., 2019.Cumida:an extensively curated microarraydatabase for benchmarking and testing of machine learning approaches in cancer research. Journal of Computational Biology, 26(4), pp.376-386.
Feng, Y., Yang, F., Zhou, X., Guo, Y., Tang, F., Ren, F., Guo, J. and Ji, S., 2018. A deep learning approach for targeted contrast-enhanced ultrasound based prostate cancer detection. IEEE/ACM transactions on computational biology and bioinformatics, 16(6), pp.1794-1801.
Goyal, H., Mann, R., Gandhi, Z., Perisetti, A., Ali, A., Aman Ali, K., Sharma, N., Saligram, S., Tharian, B. and Inamdar, S., 2020. Scope of artificial intelligence in screening and diagnosis of colorectal cancer. Journal of clinical medicine, 9(10), p.3313.
Ibrahim, I. and Abdulazeez, A., 2021. The role of machine learning algorithms for diagnosing diseases. Journal of Applied Science and Technology Trends, 2(01), pp.10-19.
Joshi, T.N. and Chawan, P.P.M., 2018. Diabetes prediction using machine learning techniques. Ijera, 8(1), pp.9-13.
Koromina, M., Pandi, M.T. and Patrinos, G.P., 2019. Rethinking drug repositioning and development with artificial intelligence, machine learning, and omics. Omics: a journal of integrative biology, 23(11), pp.539-548.
Kosvyra, A., Maramis, C. and Chouvarda, I., 2019. Developing an integrated genomic profile for cancer patients with the use of NGS data. Emerging Science Journal, 3(3), pp.157-167.
Lin, J., Tao, X. and Pan, J., 2022. An artificial intelligence-based system assisted endoscopists to detect early gastric cancer: a case report. Journal of Digital Health, pp.25-29.
Liu, R., Zhang, G. and Yang, Z., 2019. Towards rapid prediction of drug-resistant cancer cell phenotypes: single cell mass spectrometry combined with machine learning. Chemical communications, 55(5), pp.616-619.
Mitsala, A., Tsalikidis, C., Pitiakoudis, M., Simopoulos, C. and Tsaroucha, A.K., 2021. Artificial intelligence in colorectal cancer screening, diagnosis and treatment. A new era. Current Oncology, 28(3), pp.1581-1607.
Ojha, S., 2022. Recent Advancements in Artificial Intelligence Assisted Monitoring of Heart Abnormalities and Cardiovascular Diseases: A Review.
Olaniyan, O.O., 2019. Stratification of chronic myeloid leukemia cancer dataset into risk groups using four machine learning algorithms with minimal loss function.
POPESCU, F. and ICHIMESCU, C., 2021. Building, training and validation an artificial intelligence-assisted Early Warning System for COVID-19 pandemic management. Romanian Journal of Information Technology and Automatic Control, 31(2), pp.7-20.
Rahane, W., Dalvi, H., Magar, Y., Kalane, A. and Jondhale, S., 2018, March. Lung cancer detection using image processing and machine learning healthcare. In 2018 International Conference on Current Trends towards Converging Technologies (ICCTCT) (pp. 1-5). IEEE.
Rehman, O., Zhuang, H., Muhamed Ali, A., Ibrahim, A. and Li, Z., 2019. Validation of miRNAs as breast cancer biomarkers with a machine learning approach. Cancers, 11(3), p.431.
Shafique, S. and Tehsin, S., 2018. Acute lymphoblastic leukemia detection and classification of its subtypes using pretrained deep convolutional neural networks. Technology in cancer research & treatment, 17, p.1533033818802789.
Shekar, B.H. and Dagnew, G., 2019, February. Grid search-based hyperparameter tuning and classification of microarray cancer data. In 2019 second international conference on advanced computational and communication paradigms (ICACCP) (pp. 1-8). IEEE.
Thanh, T.T.P., Vununu, C., Atoev, S., Lee, S.H. and Kwon, K.R., 2018. Leukemia blood cell image classification using convolutional neural network. International Journal of Computer Theory and Engineering, 10(2), pp.54-58.
Visaggi, P., Barberio, B., Ghisa, M., Ribolsi, M., Savarino, V., Fassan, M., Valmasoni, M., Marchi, S., de Bortoli, N. and Savarino, E., 2021. Modern diagnosis of early esophageal cancer: from blood biomarkers to advanced endoscopy and artificial intelligence. Cancers, 13(13), p.3162.
Vougas, K., Sakellaropoulos, T., Kotsinas, A., Foukas, G.R.P., Ntargaras, A., Koinis, F., Polyzos, A., Myrianthopoulos, V., Zhou, H., Narang, S. and Georgoulias, V., 2019. Machine learning and data mining frameworks for predicting drug response in cancer: An overview and a novel in silico screening process based on association rule mining. Pharmacology & therapeutics, 203, p.107395. 9
Wu, J., Zhang, P., Zhang, L., Meng, W., Li, J., Tong, C., Li, Y., Cai, J., Yang, Z., Zhu, J. and Zhao, M., 2020. Rapid and accurate identification of COVID-19 infection through machine learning based on clinical available blood test results. MedRxiv.
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