Streamlined Classification of Microscopic Blood Cell Images
Keywords:
Deep Learning, Autoimmune diseases, WBC, Convolutional Neural NetworkAbstract
Deep learning is a kind of AI that mimics how humans learn certain things. Unlike traditional machine learning algorithms, deep learning algorithms are piled high with increasing complexity and abstraction. Image classification is a subject of Deep Learning where we may categorize photos into classes based on their attributes. White blood cells are a component of the body. They help the body fight infections and disorders. Neutrophils, lymphocytes, monocytes, and eosinophils are white blood cells. The classification algorithm may be used to classify current data based on coaching knowledge. A software learns from a dataset or collection of observations and then classifies fresh data into classes or groups. This work aims to propose a newly deviced CNN model called LYMPONET which has been employed to differentiate the types of white blood cells, which may be used to predict many autoimmune disorder. LYMPONET is a bespoke CNN-based architecture that performs better than existing CNN models like VGG16, InceptionV3, Xception, and ResNet152V2, as measured by performance measures.
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