Hybrid RID Network for Efficient Diagnosis of Tuberculosis from Chest X-rays

Technology nowadays is revolving around intelligent systems that mimic the learning capability of human brain. These systems learn, adapt, act and make decisions autonomously instead of just executing predefined programmed instructions. This paper presents such intelligent computer-aided diagnostic (CAD) system, named as RID network, that learns how to distinguish between normal and Tuberculosis (TB) infected radiograph. Such systems can help reducing TB epidemic as it is a curable disease and early diagnosis is a critical step towards its prevention and cure. The proposed CAD system is an ensemble created by feature-level fusion of three deep neural network models: ResNet, Inception-ResNet and DenseNet. The models were used as feature extractors and support vector machine (SVM) was used as a classifier. The methodology was tested on publically available Shenzhen dataset, which was randomly split into a 90:10 ratio as training and testing set respectively. The process was repeated 10 times with random split data to calculate the average accuracy. The algorithm achieved 90.5% average accuracy that is among top accuracies achieved till date and hence, proved its robustness and competence.