article · Multimedia Tools and Applications
Early detection and accurate diagnosis of leukaemia are critical for effective treatment, yet standard laboratory procedures remain laborious and specialised equipment such as flow cytometers is scarce. A machine learning model has been created to detect and classify blood microscopic images into normal and abnormal categories. The workflow includes three primary stages: image preprocessing, feature extraction, and classification. An optimised convolutional neural network, with hyperparameters tuned using fuzzy logic, performs the classification. This fuzzy optimisation enhances network performance substantially. When evaluated on the C-NMC_Leukemia dataset, the optimised model achieved an accuracy of 99.99 per cent in distinguishing leukaemia-free samples from leukaemia-affected samples.
Leukaemia is a cancer of blood-forming tissues affecting the bone marrow and lymphatic system. Standard detection methods often rely on scarce diagnostic equipment and time-consuming manual laboratory reviews. Automating the analysis of blood microscopic images through machine learning can improve diagnostic accuracy while reducing costs, potentially enabling earlier detection when the disease can be treated more effectively.
This classification approach could enable automated, lower-cost screening tools for laboratory diagnostic centres examining blood microscopic images. It may reduce reliance on scarce flow cytometry devices for initial analysis. Because the model has only been evaluated on the C-NMC_Leukemia benchmark dataset, the work is at an early research stage and would require clinical testing and validation before integration into commercial diagnostic workflows.
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Abstract A significant issue in the field of illness diagnostics is the early detection and diagnosis of leukemia, that is, the accurate distinction of malignant leukocytes with minimal costs in the early stages of the disease. Flow cytometer equipment is few, and the methods used at laboratory diagnostic centers are laborious despite the high prevalence of leukemia. The present systematic review was carried out to review the works intending to identify and categories leukemia by utilizing machine learning. It was motivated by the potential of machine learning (machine learning (ML)) in disease diagnosis. Leukemia is a blood-forming tissues cancer that affects the bone marrow and lymphatic system. It can be treated more effectively if it is detected early. This work developed a new classification model for blood microscopic pictures that distinguishes between leukemia-free and leukemia-affected images. The general proposed method in this paper consists of three main steps which are: (i) Image_Preprocessing, (ii) Feature Extraction, and (iii) Classification. An optimized CNN (OCNN) is used for classification. OCNN is utilized to detect and classify the photo as "normal" or "abnormal". Fuzzy optimization is used to optimize the hyperparameters of CNN. It is a quite beneficial to use fuzzy logic in the optimization of CNN. As illustrated from results it is shown that, with the using of OCNN classifier and after the optimization of the hyperparameters of the CNN, it achieved the best results due to the enhancement of the performance of the CNN. The OCNN has achieved 99.99% accuracy with C-NMC_Leukemia dataset.
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DOI: 10.1007/s11042-023-15923-8
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