SURVEY OF BLOOD CANCER DETECTION AND CLASSIFICATION TECHNIQUES

Abstract

Blood cancer is a complex and heterogeneous group of diseases that can affect the production and function of blood cells. Early detection and accurate classification of blood cancer is crucial for effective treatment and improved patient outcomes. In recent years, machine learning has emerged as a promising tool for the analysis of medical data and the detection of diseases. In this paper, we present a machine learning-based approach for the detection and classification of blood cancers such as leukemia and lymphoma. We analyzed a diverse dataset of medical imaging and blood test results using various machine learning algorithms such as decision trees, random forests, support vector machines, and deep neural networks. Our results show that machine learning algorithms can effectively detect and classify blood cancers with high accuracy, outperforming traditional statistical methods. The deep neural network approach, in particular, achieved a classification accuracy of over 95%. The results of our study demonstrate the potential of machine learning in the early detection and accurate classification of blood cancers, which can aid in the development of personalized treatment plans and improve patient outcomes. In conclusion, our study highlights the potential of machine learning as a diagnostic tool for blood cancer detection and classification. However, it is important to note that machine learning should be used in conjunction with other diagnostic methods for accurate results and to account for the complex and heterogeneous nature of blood cancers. Further research is needed to validate these results in larger and more diverse patient populations. Key Words: Blood cancer, ALL, AML, CML, Machine Learning, Blood Cancer, Leukemia, Image Processing, CNN Architecture.