Bovine Tuberculosis Detection in Meat Products Based on Deep Learning Models

Year 2025, Volume: 21 Issue: 1, 1 - 13, 26.07.2025

Mustafa Burkay Özdemir , Ekin Ekinci , Zeynep Garip , Furkan Göz

Abstract

Bovine tuberculosis (bTB) is an infectious disease that threatens human health and possibly leads to death. It is known that tuberculosis can be transmitted to a healthy person through the air. However, it can also be transmitted through the consumption of animal products, such as meat and milk, which contain the bacteria that cause tuberculosis. Detecting tuberculosis in animal products is challenging and requires specialized expertise. There are many studies in which the detection of tuberculosis is performed using Deep Learning (DL) approaches. However, there is a lack of studies on the detection of bTB in animal products. In this study, we address this problem and propose a Convolutional Neural Network (CNN) DL approach for detecting bTB in meat products. We conducted experiments on a new dataset containing images of bTB infected and healthy meats. We evaluate the performance of different CNN architectures such as ResNet-50, DarkNet-53, MobileNet-v2, GoogleNet, and EfficientNet-b0 in detecting tuberculosis in meat products with respect to several metrics. We have been able to achieve validation accuracies of 100%, 99.67%,100%, 98.04%, and 100%, for ResNet-50, DarkNet-53, MobileNet-v2, GoogleNet and EfficientNet-b0, respectively.

Keywords

deep learning, classification, bovine tuberculosis, meat products

References

• [1] Global tuberculosis report 2023 - iris home. Available at: https://iris.who.int/bitstream/handle/10665/373828/9789240083851-eng.pdf (Accessed: 03 November 2024).
• [2] X. F. Muñiz et al., "Replication and transmission features of two experimental vaccine candidates against bovine tuberculosis subcutaneously administrated in a murine model," Tuberculosis, vol. 134, p. 102203, May 2022.
• [3] L.M. O’Reilly and C.J. Daborn, "The epidemiology of mycobacterium bovis infections in animals and man: A Review", Tubercle and Lung Disease, vol. 76, no. 1, pp. 1–46, Aug. 1995.
• [4] A. K. Refaya et al., "A review on bovine tuberculosis in India," Tuberculosis, vol. 122, p. 101923, Mar. 2020.
• [5] E. I. Obeagu, "Tuberculosis diagnostic and treatment delays among patients in Uganda," Health Science Reports, vol. 6, no. 11, Nov. 2023.
• [6] M. Shegaze, B. Boda, G. Ayele, F. Gebremeskel, B. Tariku, and T. Gultie, "Why people die of active tuberculosis in the era of effective chemotherapy in Southern Ethiopia: A qualitative study," Journal of Clinical Tuberculosis and Other Mycobacterial Diseases, vol. 29, p. 100338, Nov. 2022.
• [7] B. B. Staff and B. B. Staff, “Bovine tuberculosis: a continuing threat to cattle around the world,” Behind the Bench, May 15, 2023. https://www.thermofisher.com/blog/behindthebench/bovine-tuberculosis-a-continuing-threat-to-cattle-around-the-world/.
• [8] R. Miotto, F. Wang, S. Wang, X. Jiang, and J. T. Dudley, "Deep learning for healthcare: review, opportunities and challenges," Briefings in Bioinformatics, vol. 19, no. 6, pp. 1236–1246, Apr. 2017.
• [9] S. Shamshirband, M. Fathi, A. Dehzangi, A. T. Chronopoulos, and H. Alinejad-Rokny, "A review on deep learning approaches in healthcare systems: Taxonomies, challenges, and open issues," Journal of Biomedical Informatics, vol. 113, p. 103627, Nov. 2020.
• [10] A. A. El-Solh, C.-B. Hsiao, S. Goodnough, J. Serghani, and B. J. B. Grant, "Predictingactive pulmonary tuberculosis using an artificial neural network," CHEST Journal, vol. 116, no. 4, pp. 968–973, Oct. 1999.
• [11] C. Liu et al., "TX-CNN: Detecting tuberculosis in chest X-ray images using convolutional neural network," 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China, 2017, pp. 2314-2318
• [12] A. H. Al-Timemy, R. N. Khushaba, Z. M. Mosa, and J. Escudero, "An efficient mixture of deep and machine learning models for COVID-19 and tuberculosis detection using X-Ray images in resource limited settings," in Studies in systems, decision and control, 2021, pp. 77–100.
• [13] T. Rahman et al., "Reliable tuberculosis detection using chest X-Ray with deep learning, segmentation and visualization," IEEE Access, vol. 8, pp. 191586–191601, Jan. 2020.
• [14] A. Iqbal, M. Usman, and Z. Ahmed, "Tuberculosis chest X-ray detection using CNN-based hybrid segmentation and classification approach, " Biomedical Signal Processing and Control, vol. 84, p. 104667, Mar. 2023.
• [15] S. I. Nafisah and G. Muhammad, "Tuberculosis detection in chest radiograph using convolutional neural network architecture and explainable artificial intelligence," Neural Computing and Applications, vol. 36, no. 1, pp. 111–131, Apr. 2022.
• [16] M. Momeny et al., "Greedy Autoaugment for classification of mycobacterium tuberculosis image via generalized deep CNN using mixed pooling based on minimum square rough entropy, " Computers in Biology and Medicine, vol. 141, p. 105175, Dec. 2021.
• [17] A. V. A. Bezerra, E. M. D. Reis, R. O. Rodrigues, A. Cenci, C. Cerva, and F. Q. Mayer, "Detection of Mycobacterium tuberculosis and Mycobacterium avium Complexes by Real-Time PCR in Bovine Milk from Brazilian Dairy Farms, " Journal of Food Protection, vol. 78, no. 5, pp. 1037–1042, May 2015.
• [18] I. A. Sevilla, E. Molina, M. Tello, N. Elguezabal, R. A. Juste, and J. M. Garrido, "Detection of mycobacteria by culture and DNA-Based methods in Animal-Derived food products purchased at Spanish supermarkets," Frontiers in Microbiology, vol. 8, Jun. 2017.
• [19] A. Khan, A. Sohail, U. Zahoora, and A. S. Qureshi, "A survey of the recent architectures of deep convolutional neural networks," Artificial Intelligence Review, vol. 53, no. 8, pp. 5455–5516, Apr. 2020.
• [20] Y. Li and H. Chen, "Image recognition based on deep residual shrinkage Network," 2021 International Conference on Artificial Intelligence and Electromechanical Automation (AIEA), Guangzhou, China, 2021, pp. 334-337
• [21] J. Redmon and A. Farhadi, "YOLOV3: an incremental improvement," arXiv (Cornell University), Jan. 2018, doi: 10.48550/arxiv.1804.02767. N. B. Vargafik, J. A. Wiebelt, and J. F. Malloy, "Radiative transfer," in Convective Heat. Melbourne: Engineering Education Australia, 2011, ch. 9, pp. 379–398.
• [22] M. Sandler, A. Howard, M. Zhu, A. Zhmoginov and L. -C. Chen, "MobileNetV2: Inverted Residuals and Linear Bottlenecks," 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 2018, pp. 4510-4520.
• [23] C. Szegedy et al., "Going deeper with convolutions," IEEE conference on computer vision and pattern recognition, Boston, USA, 2015, pp. 1–9.
• [24] M. Tan and Q. Le, "Efficientnet: Rethinking model scaling for convolutional neural networks," 36th International Conference on Machine Learning, Long Beach, California, 2019, pp. 6105–6114.
• [25] S. Y. Şen and N. Özkurt, "Convolutional Neural Network Hyperparameter Tuning with Adam Optimizer for ECG Classification," 2020 Innovations in Intelligent Systems and Applications Conference (ASYU), Istanbul, Turkey, 2020, pp. 1-6.