Tuberculosis Diagnosis: Updates and Challenges

Shivakumar, Prakruthi; Shettigar, Kavitha Sunil

doi:10.5772/intechopen.107168

Cited by 1 publication

(1 citation statement)

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“…Deep learning and transfer learning technologies have transformed illness categorization and offered new opportunities for medical practitioners [9][10][11]. The Chest Disease Classifier Network (CDC Net) is a revolutionary multi-classification model based on convolutional neural network (CNN) that combines the residual network with dilated convolution [12].…”

Section: Introductionmentioning

confidence: 99%

Optimizing chest tuberculosis image classification with oversampling and transfer learning

Alqahtani,

Abu Al‐Haija,

Alsulami

et al. 2023

IET Image Processing

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Tuberculosis (TB) is an extremely contagious illness caused by Mycobacterium tuberculosis. Chest tuberculosis classification is conducted based on a deep convolutional neural network architecture. In this research, a pre‐trained network is utilized to demonstrate the advantage of using the oversampling technique on the classification of TB and compare results with recent research that used the same dataset. Therefore, the dataset consists of 3500 uninfected TB cases and 700 infected with TB. This paper circumvents the imbalance by using the oversampling technique in X‐ray TB images to be fed into several pre‐trained networks for TB classification. The oversampling technique is crucial in enhancing the performance of TB classification compared with other pre‐trained models reported here. Inceptionv3 shows a promising result compared to other pre‐trained models; it achieves 99.94% accuracy, 99.88% precision, 100% recall, and 99.94% F1‐Score.

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