Blood classification plays a pivotal role in diagnosing various blood-related diseases, including Acute Lymphoblastic Leukemia (ALL), Acute Promyelocytic Leukemia (APML), Chronic Myeloid Leukemia (CML), Iron Deficiency Anemia (IDA), Thalassemia Major, Thalassemia Minor, and Normal Blood Cells. The traditional approach to blood classification involves manual analysis by hematologists. This intensive work process takes time and sometimes requires immediate diagnosis for cases that can be fatal. Therefore, to streamline this process, deep learning and image processing techniques will be utilized. This project aims to address these challenges by developing a web-based expert system for the automatic classification of blood samples using High-Resolution Neural Networks (HRNet). Inspired by the HRNet architecture originally designed for computer vision tasks, the system maintains high-resolution representations throughout the classification process. An innovative approach connects high-to-low resolution convolution streams in parallel, facilitating the preservation of high-resolution information. The exchange of information across resolutions enhances the semantic richness and spatial precision of the resulting representations. The system is capable of classifying blood samples into seven distinct categories as mentioned earlier. The proposed web application streamlines the diagnostic process, offering a faster and more accurate classification of blood samples. By automating this critical task, it supports hematologists in making more efficient diagnoses and helps prevent the progression of conditions like CML into more dangerous and acute phases. The dataset used is divided into a ratio of 8:1:1, representing the training, validation, and testing datasets. The total number of images used for training, validation, and testing are 549, 66, and 75, respectively. Thus, the total image dataset used in this project is 690. The system is targeted to achieve an accuracy rate of 99.89\%, demonstrating its effectiveness in blood classification. The HemaClassify system utilizes a REST architecture, with the frontend developed using NextJS, TailwindCSS, and ShadCN UI, and the backend implemented with a Flask API and the HRNet model. MySQL is used for database management. The user interface (UI) has been evaluated through User Acceptance Testing (UAT) conducted by Medical Laboratory Technologists (MLT) and hematologists from Hospital UKM, ensuring the system meets the requirements and expectations of end-users. The UAT results indicate a high level of user satisfaction, with most users rating the system 4/5 or 5/5 for its usability and functionality. This project represents a significant advancement in the field of blood classification, contributing to more timely and precise diagnoses, which are essential for effective disease management and treatment.
Arjunan et al. (Mon,) studied this question.