Image Analysis of Cells Acute Lymphoblastic Leukemia Using Ensemble Learning of Deep Bagging

Leukemia (ALL) is a form of blood cancer that claimed the lives of 111,000 people worldwide in 2015. Recent advances in deep learning (DL) techniques have made it possible to diagnose Everything using microscopic image analysis. However, as with most medical issues, there are deficiency training samples and visual flaws that distinguish leukaemia from normal. The body is made up of cells, making image analysis a difficult task. To address the aforementioned issues, an augmented image enhanced bagging ensemble learning with elaborately developed training subsets was proposed. The preliminary and final Fl-scores are 0.85 and 0.89, respectively, in the calculated tests. In the Classification of Normal and Malignant WBCs contest, we used our ensemble model predictions and placed in the top ten percent. Our findings show that using Deep learning-based techniques to analyse leukaemia (ALL) cells images can be efficient.