Blood Cells Classification using Convolutional neural network Architecture
White blood cells present in human body. It protect from infections by eliminating viruses, bacteria, parasites and fungi. Deep learning frameworks are proposed for the detection and classification of blood cells using blood cell images. The classification of blood cell images is done using Convolutional Neural Network, LeNet and AlexNet. In the proposed framework, the Convolutional layer and pooling layer is used for extracting the feature. The extracted features are fed into a fully connected layer for classification of Lymphocytes, Monocytes, Eosinophils, and, Neutrophils. The models are trained, validated and tested for classification of blood cell images. The proposed framework is based on a comparison of Convolutional Neural Network with different architecture. The comparisons are made to evaluate the performance of the proposed framework. It has been observed that the proposed framework will outperform the comparison of Convolutional Neural Network with different architecture such as LeNet and AlexNet in terms of accuracy and loss in detection and classification of blood cell using blood cell images.