Automated diagnosis of tuberculosis (TB) from chest X-Rays (CXR) has been tackled with either handcrafted algorithms or machine learning approaches such as support vector machines (SVMs) and convolutional neural networks (CNNs). Most deep neural network applied to the task of tuberculosis diagnosis have been adapted from natural image classification. These models have a large number of parameters as well as high hardware requirements, which makes them prone to overfitting and harder to deploy in mobile settings. We propose a simple convolutional neural network optimized for the problem which is faster and more efficient than previous models but preserves their accuracy. Moreover, the visualization capabilities of CNNs have not been fully investigated. We test saliency maps and gradCAMs as tuberculosis visualization methods, and discuss them from a radiological perspective. Tuberculosis is classified as the fifth leading cause of death worldwide, with about 10 million new cases and 1.5 million deaths per year 1 . Being one of the world's biggest threats and being rather easy to cure, the World Health Organization recommends systematic and broad use of screening to extirpate the disease. Posteroanterior chest radiography, in spite its low specificity and difficult interpertation 1 , is one of the preferred tuberculosis screening methods. Unfortunately, since TB is primarily a disease of poor countries, the clinical officers trained to interpret these chest X-Rays are often lacking 2, 3 . In these settings, an automated algorithm for tuberculosis diagnosis could be an inexpensive and effective method to make widespread tuberculosis screening a reality. As a consequence, the topic has attracted the attention of the machine learning community, which, in a range of publications [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] , tackled the problem with methods ranging from hand-crafted algorithm to support vector machines and convolutional neural networks. The results are encouraging, as some of these methods achieve nearly-human sensitivities and specificities. Over the last years, end-to-end trained convolutional neural networks (CNNs) have shown drastically superior performance on a multitude of image analysis challenges when compared to more classical hand-crafted algorithms or even other machine learning approaches such as support vector machines, in particular when the challenge can be sufficiently well characterized by abundant labeled training data. This makes deep learning a promising approach for medical image analysis 9 and showed state-of-the art performances in tasks spanning from breast cancer classification 13 , organ 14 and tumor segmentation 15, 16 to scan time reduction for diffusion MRI 17 , to name a few. The use of deep learning on chest X-Rays has attracted some attention 18, 19 due to the cheapness of this imaging technique, the abundance of data 20 and the similarity to natural images, which allows the transfer of models to medical tasks. The effectiveness of these algorithms on chest x-ray data has been shown in various publications, with some even generating automatic annotations for the symtoms 18 . For the case of tuberculosis diagnosis, deep convolutional networks have demonstrated performances at least on par with those of the best competing approaches, while being conceptually simpler. Competing approaches often make use of complex machine learning pipelines 3, 4, 8, 11 . For instance, Vajda et al. 11 use a pipeline that starts with an atlas based lung segmentation algorithm, then extracts manually selected features such as shape and
REF proposed an efficient method to detect tuberculosis based on a deep neural network with less computational and memory requirements than those of other methods.
Efficient Deep Network Architectures for Fast Chest X-Ray Tuberculosis Screening and Visualization
Scientific Reports
Scientific Reports
Computer Science Medicine