Abstract
The detection of lung-related disease for radiologists is a tedious and time-consuming task. For this reason, automatic computer-aided diagnosis (CADs) systems were developed by using digital CT scan images of lungs. The detection of lung nodule patterns is an important step for the automatic development of CAD system. Currently, the patterns of lung nodule are detected through domain-expert knowledge of image processing and accuracy is also not up-to-the-mark. Therefore, a computerized CADs tool is presented in this paper to identify six different patterns of lung nodules based on multi-layer deep learning ( known as Lung-Deep) algorithms compare to state-of-the-art systems without using the technical image processing methods. A multilayer combination of the convolutional neural network (CNN), recurrent neural networks (RNNs) and softmax linear classifiers are integrated to develop the Lung-Deep without doing any pre- or post-processing steps. The Lung-Deep system is tested with manually draw radiologist contours on the 1200 images including 3250 nodules by using statistical measures. On this dataset, the higher sensitivity (SE) of 88%, specificity (SP) of 80% and 0.98 of the area under the receiver operating curve (AUC) of 0.98 are obtained compared to other systems. Hence, this proposed lung-deep system is outperformed by integrating different layers of deep learning algorithms to detect six patterns of nodules.
Highlights
Lung cancer is increasing rapidly as estimated in 2016 [1] throughout the world
It happens due to combining of the convolutional neural network (CNN), recurrent neural network (RNN) and softmax deep classifiers for detection of lung disease patterns
A new computerized lung-nodules pattern detection system using multilayer deep learning algorithms is developed in this paper for the early diagnosis of lung cancer or lungrelated disease
Summary
Lung cancer is increasing rapidly as estimated in 2016 [1] throughout the world. If lung cancer is detected at an early stage it will definitely be cured but the chances for survival rate is below or is less than 70%. The radiologists are extensively using a high-resolution computed tomography (HRCT) [2] digital imaging tool and computer-aided diagnosis (CAD) systems to detect and diagnosis lung cancer. If the clinical experts are only using HRCT scan images to diagnosis lung cancer it is a time-consuming job [3] to detect small lung nodules. The size of lung nodules is varying widely from few millimeters to several centimeters. It is difficult for radiologists to maintain the screening process during regular visits of patients
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