Bone Marrow Cell Classification Using Efficientnet B5 Transfer Learning Model
Shikha Prasher, Leema Nelson · 2023
In order to diagnose and treat a wide range of bone marrow (BM) monitor is a extremely important step. The most important and essential information is contained in the BM nucleated differential count (NDC) analysis, which is a component of the analysing of BM. In this work, the capability of a trained CNN model to classify images of BM cells is evaluated. Therefore, it is crucial to create a deep learning automated categorization system for BM cells. Traditional classification artificial intelligence algorithms, however, only generate classification results. The outcomes of the study demonstrate that the proposed method has an accuracy of 96.72%. If several BM cell types are analysed, this will be useful for tracking conditions including thrombocytopenia, polycythaemia vera, anaemia, as well as some cancers like leukaemia, lymphoma, and multiple myeloma. It helps to increase the healthy quality of life. It can significantly increase the efficiency and precision of identifying BM cells.