Transfer Learning Analysis VGG16 For the Detection of Tuberculosis

Erna Dwi Astuti, Muslim Hidayat

Abstract


- Indonesia is still one of the countries with the highest growth of TB disease in the world. TB is an infectious disease that can cause severe lung damage, even death. TB is a critical case to be detected early so that patients immediately get the proper treatment. The challenge is the difficulty in diagnosing symptoms that are not specific and similar to other diseases. Therefore, further research is needed to find a faster, more accurate, affordable TB detection method. VGG16 is one of the Convolutional Neural Network (CNN) architectures that has the characteristic of recognizing delicate patterns of chest X-ray images of TB patients. Transfer learning on VGG16 can increase the accuracy of detecting TB disease even though it uses a small amount of training data. The trial results show that the VGG16 transfer learning technique can produce better performance with an accuracy of 94%. The accuracy value can be used to benchmark that the VGG16 transfer learning technique is proven effective in detecting TB disease

Keywords


TBC, Deteksi, Detection, VGG16, Transfer Learning

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References


B. Acharya et al., “Advances in diagnosis of Tuberculosis: an update into molecular diagnosis of Mycobacterium tuberculosis,” Mol. Biol. Rep., vol. 47, no. 5, pp. 4065–4075, May 2020, doi: 10.1007/S11033-020-05413-7.

E. MacLean et al., “Advances in molecular diagnosis of tuberculosis,” J. Clin. Microbiol., vol. 58, no. 10, Oct. 2020, doi: 10.1128/JCM.01582-19.

S. Jain, P. J.-2023 I. C. on Power, and undefined 2023, “Mango leaf disease classification using deep learning hybrid model,” ieeexplore.ieee.org, 2023, Accessed: Nov. 28, 2024. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/10085869/

C. Janiesch, P. Zschech, and K. Heinrich, “Machine learning and deep learning,” Electron. Mark., vol. 31, no. 3, pp. 685–695, Sep. 2021, doi: 10.1007/S12525-021-00475-2.

M. Akhand, S. Roy, N. Siddique, M. K.- Electronics, and undefined 2021, “Facial emotion recognition using transfer learning in the deep CNN,” mdpi.com, 2021, Accessed: Nov. 28, 2024. [Online]. Available: https://www.mdpi.com/2079-9292/10/9/1036

S. Dong, P. Wang, K. A.-C. S. Review, and undefined 2021, “A survey on deep learning and its applications,” Elsevier, 2021, Accessed: Nov. 28, 2024. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S1574013721000198

J. Pardede, B. Sitohang, S. Akbar, M. K.-I. J. I. S. Appl, and undefined 2021, “Implementation of transfer learning using VGG16 on fruit ripeness detection,” academia.edu, 2021, Accessed: Nov. 28, 2024. [Online]. Available: https://www.academia.edu/download/95243023/IJISA-V13-N2-4.pdf

H. Yang, J. Ni, J. Gao, Z. Han, T. L.-S. Reports, and undefined 2021, “A novel method for peanut variety identification and classification by Improved VGG16,” nature.com, 2021, Accessed: Nov. 28, 2024. [Online]. Available: https://www.nature.com/articles/s41598-021-95240-y

R. Nahari, M. Ulum, R. A.-I. J. of Science, and undefined 2023, “Extraction of Chest Girth and Body Length Features to Estimate Goat Weight,” iasj.net, 2023, Accessed: Nov. 28, 2024. [Online]. Available: https://www.iasj.net/iasj/download/a3f0142a8788e2c7

R. Nahari, R. Alfita, … A. S.-2023 I. 9th, and undefined 2023, “Implementation of Support Vector Machine Method to Predict Harvest Readiness of Wonosalam Coffee Fruits,” ieeexplore.ieee.org, 2023, Accessed: Nov. 28, 2024. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/10420079/




DOI: https://doi.org/10.21107/triac.v12i1.28672

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