A Lightweight Convolutional Neural Network Model for Tuberculosis Bacilli Detection From Microscopic Sputum Smear Images

Abstract

This chapter describes a lightweight convolutional neural network model that automatically detects Tuberculosis (TB) bacilli from sputum smear microscopic images. According to WHO, about onefourth of the population in the universe is infected with TB, and every day five thousand people are killed due to TB disease. There are well-known recommended diagnostics are available for TB detection, among them sputum smear microscopic examination is a primary and most efficient recommended method for most of the developing and moderately developed countries. However, this manual detection method is highly error-prone and time-consuming. In this chapter, we proposed a lightweight CNN model for classifying Tuberculosis bacilli from non-bacilli objects. We adopted a Convolutional Neural Network (CNN) architecture with a skip connection of variable lengths that can identify TB bacilli from sputum smear microscopic images. The performance of the proposed model in terms of accuracy is close to the state-of-the-art. However, the number of parameters in the proposed model is significantly less than other recently proposed models. © 2021 Scrivener Publishing LLC.