An Intelligent Model for the Detection of White Blood Cells using Artificial Intelligence. (February 2021)

• Record Type: Journal Article
• Authors: Anita, ; Yadav, Anupam
• Is Part Of: Computer methods and programs in biomedicine. Volume 199(2021)
• Publication Date: 2021-02
• Subjects: White Blood Cells -- Automatic Detection -- Artificial Electric Field Algorithm -- Optimization; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2020.105893
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Automatic Detection of WBCs in Blood Cells An AI-based model for the detection of the WBCs in Blood Cells Elliptic mapping approach for developing the model Experimentation on the real life data Abstract: Background and Objective: The automatic detection and counting of white blood cells (WBCs) play a vital role in the diagnosis of hematological diseases. Computer-aided methods are prevalent in the detection of WBCs because the manual process involves several complexities. In this article, a complete automatic detection algorithm to recognize the WBCs embedded in cluttered and complicated smear images of blood is designed. Methods: The proposed algorithm uses the ellipse detection approach to approximate the presence of WBCs in the Blood. A newly designed artificial electric field algorithm with novel velocity and position bound (AEFA-C) is employed for this purpose. The problem of detection of WBCs is transformed into an optimization problem where the random candidate solutions (ellipses) are efficiently mapped. These candidate ellipses are mapped onto the edge map of the smear image, and a complete mapping is obtained using the AEFA-C algorithm. Results: The effectiveness of the AEFA-C based detector is tested over the 60 smear images of the blood, having all the five types of WBCs or leukocytes. The developed algorithm obtained an overall detection accuracy of 96.90%. Further, the robustness test is performed on the same dataset which justifies that the technique can handle the different noises with the detection accuracy of 90.33%. Also, the comparative study of the proposed detection algorithm with the state-of-art detection algorithms is carried out. Conclusions: The experimental results demonstrate the efficiency of the proposed scheme for the detection of the WBCs in terms of detection accuracy, stability, and robustness and its outperformance over the state-of-art algorithms.