*Corresponding author: ghadataqa@uomosul.edu.iq 1 http://journal.alsalam.edu.iq/index.php/ajbms Al-Salam Journal for Medical Science Journal Homepage: http://journal.alsalam.edu.iq/index.php/ajbms E-ISSN: 2959-53980, P-ISSN: 2958-0870 ABSTRACT: This article presents a preliminary report that uses minuscule images of blood tests to develop a diagnosis of leukemia. Examining through images is crucial since illnesses can be recognized and examined at an earlier stage using the images. The framework will be centered on leukemia and white blood cell illness. In fact, even the hematologist has trouble organizing the leukemic cells, and manually arranging the platelets takes a long time and is quite loose. In this way, early detection of leukemia recurrence allows the patient to receive the appropriate treatment. In order to address this problem, the framework will make use of the capabilities in small images and examine surface, geometry, shading, and quantifiable investigation modifications. These features' variations will be utilized as the classifier input. has transformed the use of images K proposes that (NN) and agglomeration. Examining a wide range of failure measures and increasing the intricacy of every system, the findings are examined. Utilizing feedforward (NN), image division is accomplished with less noise and a very sluggish conjunction rate. K-means agglomeration and (ANN) are intentionally used in this analysis to create a collection of processes that will work together to produce a much better presentation in (IS). An analysis has been conducted to determine the best rule for (IS). Keywords: White Blood Cells, Cancer, Artificial neural, Read blood cell Leukemia detection using Artificial Neural Networks in Images of Human Blood Sample Hakar J. Mohamed Salih 1 , Jahwar Y. Arif 1 , Shaimaa Q. Sabri 1 , Ghada A. Taqa 2 * , Ahmet Çınar 3 1 Department of Computer Sciences, College of Science, University of Zakho, Duhok, Iraq . 2 Department of Dental Basic Sciences, College of Dentistry, University of Mosul, Mosul, Iraq. 3 Department of Computer Engineering, College of Engineering, Fırat, University, Elazığ, Türkiye. *Corresponding Author: Ghada A. Taqa DOI: https://doi.org/10.55145/ajbms.2024.03.02.01 Received November 2023; Accepted January 2024; Available online Febuary 2024 1. INTRODUCTION Medical imaging has played a crucial role in the development of important conceptual and interpretive techniques in environmental science and medicine over the past decade [1]. During this time, there was a notable progress in the recognition, recording, sharing, analysis, and presentation of healing images. As a result, the use of computerized image processing systems for therapeutic reasons has advanced significantly [2]. The hardest part of creating restorative images is coming up with integrated frameworks for the clinical sector. Close multidisciplinary collaboration between doctors and architects is essential for the development, execution, and endorsement of intricate treatment regimens. Hence, it is imperative to utilize a technique that can effectively and expeditiously distinguish between different categories of platelets during an urgent situation [3].[4]. Currently, the method used to distinguish blood types is visual examination of extremely small platelet pictu res. Based on observable signs of blood issues, it may cause the ordering of certain blood-related diseases. One of the most feared infections in humans is cancer [5]. One form of plasma disease that can be lethal if not identified in a timely manner is leukemia [6]. Leukemia develops when the bone marrow generates an excessive amount of irregular white blood cells (WB) [7]. Grouped based on the type of WB cell that went through L remodeling. When a cancerous neoplasm is very far along, the abnormal blood cells are usually young effects, or young cells that are not working right. These cells multiply swiftly. Severe leukemia will rapidly deteriorate if treatment is delayed. Continuous leukemia allows access to young platelets, but as the illness worsens, beneficial cells are also generated. The symptoms of chronic leukemia develop gradually, and the disease takes longer to get worse [8].