ResNet18 Supported Inspection of Tuberculosis in Chest Radiographs With Integrated Deep, LBP, and DWT Features

The lung is a vital organ in human physiology and disease in lung causes various health issues. The acute disease in lung is a medical emergency and hence several methods are developed and implemented to detect the lung abnormality. Tuberculosis (TB) is one of the common lung disease and premature diagnosis and treatment is necessary to cure the disease with appropriate medication. Clinical level assessment of TB is commonly performed with chest radiographs (X-ray) and the recorded images are then examined to identify TB and its harshness. This research proposes a TB detection framework using integrated optimal deep and handcrafted features. The different stages of this work include (i) X-ray collection and processing, (ii) Pre-trained Deep-Learning (PDL) scheme-based feature mining, (iii) Feature extraction with Local Binary Pattern (LBP) and Discrete Wavelet Transform (DWT), (iv) Feature optimization with Firefly-Algorithm, (v) Feature ranking and serial concatenation, and (vi) Classification by means of a 5-fold cross confirmation. The result of this study validates that, the ResNet18 scheme helps to achieve a better accuracy with SoftMax (95.2%) classifier and Decision Tree Classifier (99%) with deep and concatenated features, respectively. Further, overall performance of Decision Tree is better compared to other classifiers.

Automated summary

The lung is vital in human physiology, and lung disease causes various health issues. Tuberculosis (TB) is a common lung disease, and early diagnosis and treatment are crucial. This study proposes a TB detection framework using integrated optimal deep and handcrafted features. The research process includes data collection and processing, feature mining using pre-trained deep learning schemes, feature extraction with LBP and DWT, feature optimization with the Firefly-Algorithm, feature ranking and concatenation, and classification using a 5-fold cross-validation. The results show that the ResNet18 scheme with SoftMax classifier achieves a better accuracy of 95.2%, while the Decision Tree Classifier reaches 99% accuracy with deep and concatenated features. Overall, the Decision Tree performs better compared to other classifiers.