Deep learning based Non-Local k-best renyi entropy for classification of white blood cell subtypes

Abstract

The major issue in classifying blood cells from the microscopic image is overlapping cells and overfitting data. Therefore, to solve these problems in microscopic imaging, a thresholding scheme based on a non-local average filter approach is used to segment cells that separate the overlap of cells and thus perform the segmentation procedure. Basic convolutional neural networks (CNN) such as AlexNet, ResNet, and DenseNet have proven to be more powerful in image classification in various fields. However, the problem is that the number of parameters used in the network increased, which tends to separate the optimization procedure; otherwise, it leads to complexity in processing (increased simulation time). The SqueezeNet architecture is used to reduce these problems and overcome the problems in blood cell classification, like the overfitting of data and gradient vanishing problems. That will extract and classify the features based on the ground truth with 5 times fewer parameters than the traditional CNN architectures. The proposed method has improved the accuracy to 99.83%, 99.67%, 99.56% and 99.02% for the test datasets of kaggle-blood cell image, LISC dataset, Raabin-WBC and BCCD dataset, respectively.