Pulla Sujarani,
M. Yogeshwari,
- Research Scholar Department of computer Science, Vels Institute of Science, Technology & Advanced Studies (VISTAS) India
- Assistant Professor Department of computer Science, Vels Institute of Science, Technology & Advanced Studies (VISTAS) India
Abstract
Blood, bone marrow, and lymphatic systems are all impacted by hematological cancer is known as a cancer blood disorder. Blood malignancies and various blood disorders pose significant health challenges across all age groups. Early disease detection is essential for effective cancer blood disorder treatment and management. If a blood cancer is not identified in time, it may be hazardous. It results in abnormal white blood cell production by the bone marrow in the blood. It is possible to diagnose blood cancer early with deep learning algorithms. Our study presents a novel and highly effective approach to predicting cancer blood disorders from medical images. The convolutional neural networks (CNN) method will be used to extract characteristics from blood samples or images, followed by deep learning algorithms to separate malignant from non-cancerous samples. Many researchers have proposed various deep learning-based techniques to improve the accuracy of blood cancer diagnosis, such as feature selection and hybrid models. Our revolutionary DCNN classification architecture trains quickly. With 98% accuracy, our method is incredibly successful. To compare our system to existing classifiers to test its performance. We developed a complete system for segmenting and predicting cancer-related blood abnormalities, exceeding current methods. Based on the results, deep learning approaches have the potential to enhance blood cancer diagnosis and therapy by achieving high detection accuracy. The study also highlights this field’s future directions. However further study is required to create more accurate and reliable models for therapeutic use.
Keywords: Blood Disorder, Deep Learning Techniques, Blood Sample Images, Classification, Convolutional Neural Network.
[This article belongs to Research & Reviews: Journal of Oncology and Hematology(rrjooh)]
References
- Madhukar, S. Agaian, and A. T. Chronopoulos, “Deterministic model for Acute Myelogenous Leukemia classification,” in 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Seoul, Korea (South), Oct. 2012, pp. 433–438. doi: 10.1109/ICSMC.2012.6377762.
- M. Olsen, L. J. Zitella, and Oncology Nursing Society, Eds., Hematologic malignancies in adults. Pittsburgh, PA: Oncology Nursing Society, 2013.
- Shafique and S. Tehsin, “Acute Lymphoblastic Leukemia Detection and Classification of Its Subtypes Using Pretrained Deep Convolutional Neural Networks,” Technol. Cancer Res. Treat., vol. 17, p. 153303381880278, Jan. 2018, doi: 10.1177/1533033818802789.
- Ferrando AA, Lopez-Otin C. Clonal evolution in leukemia. Nature medicine. 2017 Oct 1;23(10):1135-45. doi: 10.1038/nm.4410.
- Salah HT, Muhsen IN, Salama ME, Owaidah T, Hashmi SK. Machine learning applications in the diagnosis of leukemia: Current trends and future directions. International journal of laboratory hematology. 2019 Dec;41(6):717-25, doi: 10.1111/ijlh.13089.
- S. Lasya, A. Mahalakshmi, and A. Suma, “Detection and Classification of Leukemia using Convolutional Neural Network and Updation in IoT,” Int. Res. J. Eng. Technol., vol. 07, no. 03, pp. 2395–2399, Mar. 2020.
- Genovese A, Hosseini MS, Piuri V, Plataniotis KN, Scotti F. Histopathological transfer learning for acute lymphoblastic leukemia detection. In2021 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) 2021 Jun 18 (pp. 1-6). IEEE. doi: 10.1109/CIVEMSA52099.2021.9493677.
- Rehman A, Abbas N, Saba T, Rahman SI, Mehmood Z, Kolivand H. Classification of acute lymphoblastic leukemia using deep learning. Microscopy Research and Technique. 2018 Nov;81(11):1310-7, Nov. 2018, doi: 10.1002/jemt.23139.
- Boldú L, Merino A, Alférez S, Molina A, Acevedo A, Rodellar J. Automatic recognition of different types of acute leukaemia in peripheral blood by image analysis. Journal of Clinical Pathology. 2019 Nov 1;72(11):755-61, doi 10.1136/jclinpath-2019-205949.
- Di Ruberto C, Loddo A, Puglisi G. Blob detection and deep learning for leukemic blood image analysis. Applied Sciences. 2020 Feb 10;10(3):1176. Feb. 2020, doi: 10.3390/app10031176.
- Kalaiselvi K, Sujarani P. Correlation Feature Selection (CFS) and Probabilistic Neural Network (PNN) for Diabetes Disease Prediction. International Journal of Engineering & Technology. 2018 Aug;7(3.27):325-30.
Research & Reviews: Journal of Oncology and Hematology
Volume | 13 |
Issue | 02 |
Received | April 12, 2024 |
Accepted | April 27, 2024 |
Published | August 23, 2024 |