Blood Cancer Detection from Blood Smear Images using Machine Learning Algorithms

Abstract

Artificial intelligence techniques in computer vision have made a substantial contribution to the development of imaging analysis in medicine by improving the accuracy of predictions, which has resulted in more suitable treatment and diagnostics. By offering a second view, these techniques can help hematologists and other medical professionals make better diagnoses in the area of automated leukemidical imaging and cancer in the blood diagnosis. A thorough examination of the existing DM and DL image processing algorithms is provided in this study, with a particular emphasis on how to identify of leukocytes in smear blood imaging alongside various clinical imaging regions. The primary aim of the suggested investigation is to identify the best DM and DL techniques for clinical imaging, particularly for identifying the types of lymphocytes from smear blood data. This review article delves closely into the sophisticated algorithms for DL, especially the emerging models built around CNNs that operate in the clinical computational imaging area. According to a review of associated research, white blood cell identification using micro smear captures is a common application of standard AI technologies. They aid in the diagnosis of numerous illnesses, including blood cancer, and give medical professionals important information. As the researchers and professionals assigned to the processing of medical images, we construct recommendations for future investigations based on the extensive analysis of white blood cell associated research study that is laid out through the current research.