Ensemble learning based automatic detection of tuberculosis in chest X-ray images using hybrid feature descriptors.
Tuberculosis (TB) remains one of the major health problems in modern times with a high mortality rate. While efforts are being made to make early diagnosis accessible and more reliable in high burden TB countries, digital chest radiography has become a popular source for this purpose. However, the screening process requires expert radiologists which may be a potential barrier in developing countries. A fully automatic computer-aided diagnosis system can reduce the need of trained personnel for early diagnosis of TB using chest X-ray images. In this paper, we have proposed a novel TB detection technique that combines hand-crafted features with deep features (convolutional neural network-based) through Ensemble Learning. Handcrafted features were extracted via Gabor Filter and deep features were extracted via pre-trained deep learning models. Two publicly available datasets namely (i) Montgomery and (ii) Shenzhen were used to evaluate the proposed system. The proposed methodology was validated with a k-fold cross-validation scheme. The area under receiver operating characteristics curves of 0.99 and 0.97 were achieved for Shenzhen and Montgomery datasets respectively which shows the superiority of the proposed scheme.
['Ayaz|Muhammad|M|', 'Shaukat|Furqan|F|', 'Raja|Gulistan|G|http://orcid.org/0000-0003-2026-1219']
D003936:Diagnosis, Computer-Assisted D006801:Humans D000069550:Machine Learning D016571:Neural Networks, Computer D014376:Tuberculosis D014965:X-Rays
Ensemble learning Tuberculosis Computer aided diagnosis Convolutional neural network