A Pyramid Model of Convolutional Neural Network to Classify Acute Lymphoblastic Leukemia Images

Abstract

Many researchers have classified acute lymphoblastic leukemia using several methods. One of the methods is a convolutional neural network. However, the limitation of the convolutional neural network is a large number of trainable parameters updated. The paper proposes a new architecture based on the convolutional neural network. We have designed and implemented a convolutional neural network with different kernels, where we increase the number of kernels like pyramid models. We utilized the final convolution to conduct the fully connected, followed by the SoftMax function to classify the image. We have evaluated our proposed architecture using acute lymphoblastic leukemia image database (ALL-IDB2). The results show that our proposed method produced the average Accuracy, Precision, and Recall of 99.17%, 99.33%, and 99%, respectively. It has outperformed other models, i.e., shape features, Multi distance of GLCM, CNN and SVM, Pretrained deep convolutional neural networks, AlexNet, ensemble network, convolutional and recurrent neural network, and hypercomplex-valued convolutional neural networks