Abstract
Tuberculosis is a contagious disease and is one of the leading causes of death especially in low and middle income countries such as Uganda. While there are several ways to diagnose tuberculosis, sputum smear microscopy is the commonest method practised. However, this method can be error prone and also requires trained medical personnel who are not always readily available. In this research, we apply deep learning models based on two pre-trained Convolutional Neural Networks: VGGNet and GoogLeNet Inception v3 to diagnose tuberculosis from 148 Ziehl-Neelsen stained sputum smear microscopic images from two different datasets. These networks are used in three different scenarios, namely, fast feature extraction without data augmentation, fast feature extraction with data augmentation and fine-tuning. Our results show that using Inception v3 for fast feature extraction without data augmentation produces the best results with an accuracy score of 86.7%. This provides a much better approach to disease diagnosis based on the use of diverse datasets from different sources and the results of this work can be leveraged in medical imaging for faster tuberculosis diagnosis.
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Acknowledgments
This work was supported by the SIDA project 381 under the Makerere-Swedish bilateral research programme 2015–2020.
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Muyama, L., Nakatumba-Nabende, J., Mudali, D. (2021). Automated Detection of Tuberculosis from Sputum Smear Microscopic Images Using Transfer Learning Techniques. In: Abraham, A., Siarry, P., Ma, K., Kaklauskas, A. (eds) Intelligent Systems Design and Applications. ISDA 2019. Advances in Intelligent Systems and Computing, vol 1181. Springer, Cham. https://doi.org/10.1007/978-3-030-49342-4_6
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