An advanced intelligent ELISA test for bovine tuberculosis diagnosis. (September 2018)

• Record Type: Journal Article
• Authors: Sahli, Hanene; Mouelhi, Aymen; Diouani, Mohamed Fethi; Tlig, Lotfi; Refai, Amira; Landoulsi, Ramzi Boubaker; Sayadi, Mounir; Essafi, Makram
• Is Part Of: Biomedical signal processing and control. Volume 46(2018)
• Page Start: 59
• Page End: 66
• Publication Date: 2018-09
• Subjects: Bovine tuberculosis (bTB) -- Enzyme linked immuno sorbent assay (ELISA) -- Intra dermal reaction (IDR) -- Fisher's linear discriminant (FLD) -- Artificial neural network (ANN); Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2018.05.031
• Languages: English
• ISSNs: 1746-8094

Abstract

Graphical abstract: Highlights: An intelligent method based on ELISA test is proposed to enhance the bovine tuberculosis (bTB) diagnosis. The proposed method is tested on a database of serological ELISA test assessed by the IDR test. Effective classification with FANN technique in real use is the main challenge of bTB automatic analysis. Advanced system may be helpful for the veterinary experts for attentive bacteriological surveillance. Abstract: The control of bovine tuberculosis (bTB) relies first on an optimal diagnosis of the disease. Several tests have been implemented for bTB detection which are generally complex, slow of use and relatively expensive especially in poor countries. A simple rapid, cost effective and efficient automated method for bTB assessment is still needed. Here, we propose a combination of the simple Enzyme Linked Immuno Sorbent Assay (ELISA) test with either the artificial neural network (ANN) analyzing method to effectively diagnose TB in cattle. The proposed method has been experimented on 30 bTB+ and 43 bTB- subjects in the north part of Tunisia, as assessed by the intra dermal reaction test (IDR). The obtained results have reached a 94% of accuracy when applying the ANN. Moreover, the proposed methodology enabled us to reduce the number of the used pathogens-derived antigens to three instead of the standard five antigens-based ELISA. Compared to previous works, the proposed expert system seems to be promising and may prove helpful for the veterinary diagnosis of tuberculosis.