An Analysis of Image Enhancement Effects on Convolutional Neural Network-based Pulmonary Tuberculosis Detection

Abstract

Pulmonary Tuberculosis (TB) is a primary global infectious disease. Diagnosing TB patients involves medical examination and chest X-ray (CXR) imaging. This CXR image creates an opportunity to utilize machine learning to help physicians and radiologists diagnose TB suspects. Due to the inconsistency of image quality, image enhancement is one of the preprocessing steps to overcome the poor quality of the image. This study examines the effects of several image enhancement techniques, i.e., Histogram Equalization (HE), Contrast Limited Adaptive Histogram Equalization (CLAHE), and Fast Fourier Transform (FFT). These enhanced images are input for a Convolutional Neural Network (CNN). InceptionV3 is a transfer learning architecture with ImageNet as the pre-trained model. The image dataset consists of 3,500 normal and 3,500 tuberculosis CXR images. The best performance, in terms of accuracy and processing time, is achieved by the CLAHE enhancement technique, increasing accuracy by 4.57% compared to the original images as input and a processing time of 5.6 ms faster per testing image. A deeper analysis shows despite FFT achieving high performance, the processing time increases by 14.4 ms compared to the original image processing time. This study concluded that each image enhancement needs to consider the characteristics of the images.