Computer‐aided deep learning model for identification of lymphoblast cell using microscopic leukocyte images. Issue 4 (29th November 2021)

• Record Type: Journal Article
• Authors: Kumar, Abhishek; Rawat, Jyoti; Kumar, Indrajeet; Rashid, Mamoon; Singh, Kamred Udham; Al‐Otaibi, Yasser D.; Tariq, Usman
• Other Names: Chang Victor guestEditor.; Ramachandran Muthu guestEditor.; Li Chung‐Sheng guestEditor.; Zamorano Mariano Rincón guestEditor.; Tomás Rafael Martínez guestEditor.; Vicente José Manuel Ferrández guestEditor.
• Is Part Of: Expert systems. Volume 39:Issue 4(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-11-29
• Subjects: acute lymphoblastic leukaemia -- convolutional neural network -- data augmentation -- leukocyte -- lymphoblast identification; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12894
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: The conventional technique of leukocyte cell classification involves segmenting the required portion of cells from input image, extracting features of the segmented nuclei, reducing and optimizing these features and then implements the classifier. Thus, designing a good classifier by using such techniques increases the time complexity of the system. In order to resolve such issues, the proposed work implements the deep convolutional neural network (DCNN)‐based models for classifying malignant versus normal WBCs. The proposed system is validated on 108 images of ALL‐IDB 1. Due to limited number of training samples, data augmentation is used to create a similar type of virtual image. In this work, experimentation is carried out for discrimination between normal and infected WBC using DCNN with four different activation functions. By using this method, a set of 6000 samples are generated and used for proper training of the DL model for all activation functions. The performance of each trained model is evaluated in terms of accuracy, recall, precision and F‐measure with the maximum values of 98.1%, 98.3%, 98.3% and 98.3% are achieved, respectively. Finally, it has been concluded that the defined DCNN model and ReLu activation function yield outstanding performance for lymphoblast characterization using microscopic blood images.