Image Enhancement for Tuberculosis Detection Using Deep Learning

Abstract

The latest World Health Organization's (WHO) study on 2018 is showing that about 1.5 million people died and around 10 million people are infected with tuberculosis (TB) each year. Moreover, more than 4,000 people die every day from TB. A number of those deaths could have been stopped if the disease was identified sooner. In the recent literature, important work can be found on automating the diagnosis by applying techniques of deep learning (DL) to the medical images. While DL has yielded promising results in many areas, comprehensive TB diagnostic studies remain limited. DL requires a large number of high-quality training samples to yield better performance. Due to the low contrast of TB chest x-ray (CXR) images, they are often is in poor quality. This work assesses the effect of image enhancement on performance of DL technique to address this problem. The employed image enhancement algorithm was able to highlight the overall or local characteristics of the images, including some interesting features. Specifically, three image enhancement algorithms called Unsharp Masking (UM), High-Frequency Emphasis Filtering (HEF) and Contrast Limited Adaptive Histogram Equalization (CLAHE), were evaluated. The enhanced image samples were then fed to the pre-trained ResNet and EfficientNet models for transfer learning. In a TB image dataset, we achieved 89.92% and 94.8% of classification accuracy and AUC (Area Under Curve) scores, respectively. All the results are obtained using Shenzhen dataset, which are available in the public domain.

Partial Color Photo Processing Method for Components Based on Image Enhancement Technology

“…To a certain extent, it solves the low-quality problem of taking photos in rainy days at night. Literature [34] is aimed at enhancing the clarity of medical images, highlighting the local and overall characteristics of medical imaging, making the model pay attention to the region of medical interest, and using the pretraining model RESNET and efficient net model training network to enhance the image. The problem of the low contrast of chest X-ray image of pulmonary tuberculosis was solved.…”

A Novel Machine Learning Approach for Tuberculosis Segmentation and Prediction Using Chest-X-Ray (CXR) Images

“…Munadi et al [9] explained some algorithms used to enhance images of CXRs to detect tuberculosis. These algorithms were evaluated by unsharp masking (UM), high-frequency emphasis filtering (HEF), and contrast limited adaptive histogram equalization (CLAHE).…”

“…Several morphological operations have been represented as combinations of erosion, dilation, and simple set-theoretic operations such as the complement of a binary image. These were mentioned in Equations ( 6)- (9).…”

Data-Efficient Training of High-Resolution Images in Medical Domain

“…High-resolution images are desirable [7], [8] as they yield better network performance and provide valuable insights into identifying a particular diseases. There have been few attempts earlier to alleviate batch size problem in such scenarios.…”

“…In [3], the authors implemented spatial partitioning in a Mesh-TensorFlow framework. The study in [7] concludes that increasing image resolution for CNN training often has a trade-off with the maximum possible batch size, but this is amenable to optimization for maximization of neural network performance. In essence, earlier works have mostly focused on devising ways to train deep networks on high-resolution images with effective GPU utilization.…”