Image classification enhancement of blood cell cancer using VGG19

The enormous volume of data, the reduction of human error, and the high classification throughput have made the automation of blood cell cancer image classification and recognition more and more popular. This work uses a number of deep learning (DL) models from Convolutional Neural Networks (CNNs) to test the classification accuracy. Alexnet, Resnet50, Visual Geometry Group 16 (VGG16), Visual Geometry Group 19 (VGG19), and GoogLeNet are the models. Various convolutional layers, including the Fully Connected (FC), max pooling, and soft-max layers, make up the architecture of these models. Since Vgg19 model showed the highest accuracy of classification with epoch size= 30, Minimum Batch Size (MBS)= 64, learning rate= 0.001 and Sgdm optimizer, it has been modified for less complexity and higher performance than its based model. The confusion matrix is used to assess and display the performance of the base models and the modified Vgg19 model. Four types of blood cell cancered images were used in this work, these are: Eosinophil, Neutrophil, Eosinophil and Monocyte. Modified Vgg19 achieved an accuracy of 0.983 and complexity in term of FLOPs is reduced to 0.7% of the original complexity for Vgg19 based model.