International Journal of Advanced Technology and Engineering Exploration, Vol 9(95) ISSN (Print): 2394-5443 ISSN (Online): 2394-7454 http://dx.doi.org/10.19101/IJATEE.2021.875564 1448 i-Net: a deep CNN model for white blood cancer segmentation and classification Agughasi Victor Ikechukwu * and Murali S. Department of Computer Science, Maharaja Institute of Technology Mysore-571477 Karnataka, India 1 Received: 28-February-2022; Revised: 20-October-2022; Accepted: 22-October-2022 ©2022 Agughasi Victor Ikechukwu and Murali S. This is an open access article distributed under the Creative Commons Attribution (CC BY) License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1.Introduction The blood, which is the lifeline of humans consists of the plasma, platelets, red blood corpuscles (RBC), and white blood corpuscles (WBC) along with another immunoglobulin. Leukaemia is a kind of blood deficiency that is usually chronic. The prevalence of leukaemia varies based on the type of disease and the demographics of the population [1]. The major cause of anaemia is blood cell proliferation, which is hindered by rapid expansion of defective blood cells [2]. *Author for correspondence Cancer of the blood primarily leukaemia, myeloma, and lymphoma with acute lymphoblastic leukaemia (ALL), a variant of blood malignancy that affects the bone marrow [1]. The term “acute” and “chronic” refers to the disease's rapid and slower progression, and if untreated at the earliest, has the potential to weaken the immune system in a short span of time. Leukaemia, the most common type of blood cancer is further divided into three, namely: L1, L2, and L3. An important plasma-rich immature teratoma that aids in the removal of infection, and three times more frequent than ALL is the multiple myeloma (MM) [4]. A decreased platelet count in the blood, a condition known as Thrombocytopenia, is a symptom of MM [5] which causes bone erosion and may be seen on CT scans as bone lesions. According to the study carried out by Ianniciello and Helgason [6], from 45 countries representing 90% of the world‟s Research Article Abstract The immune system relies on white blood cells and platelets, which are both produced in the bone marrow and together account for around one percent of the blood corpuscles. Acute lymphoblastic leukaemia (ALL) and acute myeloid leukaemia (AML) are two major subtypes of acute leukaemia identifiable from its lineage. Unlike other chronic diseases, leukaemia is a curable blood disorder and patients’ survival is possi ble with precise treatment. The effectiveness of this disease's treatment can be greatly influenced by early diagnosis. This study focused on a deep neural network for the segmentation and classification of ALL using the SN-AM and ALL-IDB datasets and obtained from the cancer imaging archive (TCIA) repository. ResNet-50 and VGG-19, two of the most popular deep learning networks, were used. The use of stored weights was not used for these two networks; instead, we modified the weights and learning parameters. A UNet with InceptionV2 model was used for the segmentation, while convolutional neural network (CNN) was employed to train the images after feature selection. An improved CNN called i-Net with more convolutional layers and tuned hyperparameters was proposed for the classification into normal and cancerous white blood cells. Data augmentation, dropout regularization, and batch normalization were employed to reduce overfitting. ResNet-50, VGG-19, and a proposed deep neural network called “i-Net”, all have validation accuracy of 92.2%, 92.3%, and 99.18%, respectively. However, when trained without early stoppage, the model (i-Net) accuracy decreased after the 30th training cycle (epoch). CNN has shown to be accurate at diagnosing ALL according to this study. When compared to other pre-trained deep learning models such as the standard VGG-19 and ResNet-50, we achieved a better performance on the test dataset of about 630 microscopic images suggesting that the CNN can be used in clinical decision support systems (CDSS) for leukaemia detection. Keywords Acute lymphoblastic leukaemia, CNN, CDSS, Data augmentation, Deep learning, Image segmentation, Medical Imaging.