Affiliations: [a] Department of Computer Science and Engineering, College of Engineering Muttathara, Thiruvananthapuram, Kerala, India
| [b] Department of Computer Science and Engineering, Mar Baselios College of Engineering and Technology, Thiruvananthapuram, Kerala, India
| [c] Department of Computer Science and Engineering, St. Thomas College of Engineering and Technology, Kannur, Kerala, India
| [d] Department of Electronics and Communication Engineering, Government Engineering College Wayanad, Kerala, India
Correspondence:
[*]
Corresponding author: Sivakumar Ramachandran, Department of Electronics and Communication Engineering, Government Engineering College Wayanad, Kerala, India. E-mail: sivan@gecwyd.ac.in.
Abstract: BACKGROUND:Tuberculosis (TB) is a highly infectious disease that mainly affects the human lungs. The gold standard for TB diagnosis is Xpert Mycobacterium tuberculosis/ resistance to rifampicin (MTB/RIF) testing. X-ray, a relatively inexpensive and widely used imaging modality, can be employed as an alternative for early diagnosis of the disease. Computer-aided techniques can be used to assist radiologists in interpreting X-ray images, which can improve the ease and accuracy of diagnosis. OBJECTIVE:To develop a computer-aided technique for the diagnosis of TB from X-ray images using deep learning techniques. METHODS:This research paper presents a novel approach for TB diagnosis from X-ray using deep learning methods. The proposed method uses an ensemble of two pre-trained neural networks, namely EfficientnetB0 and Densenet201, for feature extraction. The features extracted using two CNNs are expected to generate more accurate and representative features than a single CNN. A custom-built artificial neural network (ANN) called PatternNet with two hidden layers is utilized to classify the extracted features. RESULTS:The effectiveness of the proposed method was assessed on two publicly accessible datasets, namely the Montgomery and Shenzhen datasets. The Montgomery dataset comprises 138 X-ray images, while the Shenzhen dataset has 662 X-ray images. The method was further evaluated after combining both datasets. The method performed exceptionally well on all three datasets, achieving high Area Under the Curve (AUC) scores of 0.9978, 0.9836, and 0.9914, respectively, using a 10-fold cross-validation technique. CONCLUSION:The experiments performed in this study prove the effectiveness of features extracted using EfficientnetB0 and Densenet201 in combination with PatternNet classifier in the diagnosis of tuberculosis from X-ray images.
