Abstract:
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 research presents such
intelligent computer-aided diagnostic (CAD) system that tries to mimic the brain of a
radiologist by learning how to distinguish between normal and Tuberculosis (TB) infected
chest x-ray. Such systems can facilitate reducing TB epidemic as it is a curable disease and
early diagnosis is a critical parameter of its cure. Proposed research consists of two main
modules i.e. segmentation of lungs region and classification of x-ray as normal or TB
infected. Segmentation of lungs from chest x-rays is a crucial step in designing of such CAD
system and a fully convolutional neural network was used for this purpose. Post processing
was done to fill holes, separate left and right lung from each other and remove unwanted
objects that appeared in few cases. The process was repeated 10 times, with random split of
data into a 60:40 ratio as training and testing sets respectively, to calculate the average
accuracy. The segmentation methodology was tested on three datasets: Japanese Society of
Radiological Technology (JSRT), Montgomery County (MC), and a local dataset that
achieved average accuracy of 97.1%, 97.7% and 94.2% respectively. For TB classification
purpose, an ensemble was 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, which
achieved 90.5% average accuracy that is among top accuracies achieved till date on
Shenzhen dataset. The results proved that the proposed methodologies are efficient enough
and can be generalized for other such segmentation and classification problems in medical
imaging domain.
