Int. J. Advanced Networking and Applications Volume: 16 Issue: 05 Pages: 6602-6614 (2025) ISSN: 0975-0290 6602 Classifying Blood Cancer from Blood Smear Images using Artificial Intelligence Algorithms Amira Hassan Abed Business Information Systems Department, Faculty of Business Administration, Al-Ryada University for science and technology, Al Sadat City, Egypt; Email Address: Amira.Abed@rst.edu.eg. -------------------------------------------------------------------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. Keywords - blood cancer, Leukemidical image processing, medical image processing, Smear Blood images, AI, CNN, deep learning (DL), Data Mining (DM), white blood cells classification. -------------------------------------------------------------------------------------------------------------------------------------------------- Date of Submission: 17 Feb 2025 Date of Acceptance: 27 Feb 2025 -------------------------------------------------------------------------------------------------------------------------------------------------- I. INTRODUCTION Artificial intelligence (AI) approaches have become commonplace for many purposes, including the interpretation of images from hospitals [1]. Medical imaging evaluation has become a crucial component of contemporary healthcare systems, providing intelligent support to medical specialists. Image analysis in medicine processes many image modalities, including CT scans, MRIs, blood smear pictures, and ultrasounds, and is essential in the diagnosis of numerous disorders [2]. Regarding diagnosis and academic objectives, imaging paradigms are essential in medical imaging evaluation because they help identify and categorize both soft and hard tissue of various human organs [3]. Professionals in computer vision have a lot to contribute to the analysis of medical images. AI is important for leukocytes identification of cancer, healthcare data commentary, and retrieving images into computer-assisted diagnostics. Since the efficacy of AI algorithms immediately impacts medical diagnosis and therapy processes, the use of computers in detection depends on them [4], [5]. It facilitates how doctors use conventional working methods in addition helps them even more with diagnosis and therapy. Computer-aided design is greatly impacted by contemporary technological advancements in device fashion, capacity for storage, as well as high rates of processing power. Leukocyte characterization using MRI scans were previously important applications for the Computer-aided design system using AI. This gives medical professionals crucial knowledge that aids in the diagnosis of many hematic issues, including blood-related cancers. Images processing's primary goal is to efficiently support clinicians during the diagnosis procedure. Within the medical community, leukocytes or WBCs are seen to be a good indicator of most human disorders. Diverse individual illnesses can be identified by variations within the dimensions, form, and hue of leukocytes as well as by changes in their geometrical structure, as seen in smear imaging. Different blood cell types exist, including leukocytes, which are further classified into five subtypes. Different AI methods have been developed for recognizing classifying white blood cells in images of micro blood smears. Traditional methods rely on the laborious, difficult, and error-prone manual examination of WBCs in images of smear blood [6]–[9]. Diagnostic procedures along with suitable therapy are greatly aided by automatic systems [10]. As a result, automated Leukocyte detection