Machine learning in detection and classification of leukemia using C-NMC_Leukemia

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.