Comparative Study of Cancer Blood Disorder Detection Using Convolutional Neural Networks

Abstract

Blood malignancies and various blood disorders can have an impact on a person. It is a major health issue in all age groups. A blood disorder, such as influence platelets, blood plasma, and white and red blood cells, can impact any of the four primary blood components. The primary goal of this chapter is to detect the cancer blood disorder. This paved the way to propose a comparative study with previous studies based on convolutional neural networks in this work. The authors propose a model for cancer blood disorder detection. It consists of five steps. The blood sample image data set is collected from the Kaggle. First, the data set is transferred for image preprocessing to remove the noise from the images. Next, it is applied to the image enhancement for clarity; the image and segmentation are performed on enhanced images. Next, feature selection is used to extract the features from the segmentation images. The convolutional neural network technique is used for classification finally.