Abstract
Maintaining a healthy life is very important in today’s world. Our body has ability to fight against contagious diseases with the aid of white blood corpuscles (WBC) which generates natural immune system. Preserving a good WBC count is crucial as it leads to various hematological problems, one among them is Leukemia, a condition which results in blood cancer, huge accumulation of WBC cells in bone marrow, an abnormal growth of WBC cells, hinders natural immune system fighting against infectious diseases. White blood cells can be categorised into Eosinophils, Lymphocytes, Monocytes, Neutrophils and Basophils. In this study, a pre-trained Convolutional neural network architecture called Le Net is used to automatically classify WBC cells. It efficiently classifies WBC cells from the given input sample blood cell images compared to other pre trained models such as Alex-Net and custom-built CNN called white capsule net. The hyper parameters Le Net are fine tuned to yield higher accuracy of 96%.
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