Abstract:Deep learning algorithms for blood cell detection, aiming to improve diagnostic accuracy. White Blood Cells also known as leukocytes play an important role in the human body by increasing the immunity by fighting against infectious diseases. The classification of White Blood Cells plays an important role in detection of a disease in an individual. The classification can also assist with the identification of diseases like infections, allergies, anemia, leukemia, cancer, Acquired Immune Deficiency Syndrome (AIDS), etc. that are caused due to anomalies in the immune system. This classification will assist the hematologist distinguish the type of White Blood Cells present in human body and find the root cause of diseases. Currently there is a large amount of research going on in this field. Considering a huge potential in the significance of classification of WBCs, we will be using a deep learning technique Convolution Neural Networks (CNN) which can classify the images of WBCs into its subtypes namely, Neutrophil, Eosinophil, Lymphocyte and Monocyte. In this paper, we will be reporting the results of various experiments executed on the Blood Cell Classification and Detection (BCCD) dataset using Dense Net algorithm.
Keywords: BCCD, blood cells, cancer, classification,Dense Net, Filtering
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DOI:
10.17148/IJARCCE.2024.13380
