Abstract
Simple SummaryLung cancer is the second most common cancer in men after prostate cancer and in women after breast cancer, but it is the leading cause of cancer death among both genders. This manuscript proposes a new computer-aided diagnosis system that uses only a single computed tomography scan to diagnose the pulmonary nodule as benign or malignant. This system helps in the early detection of the pulmonary nodules and shows its ability to identify the pulmonary nodules precisely.Lung cancer is one of the most dreadful cancers, and its detection in the early stage is very important and challenging. This manuscript proposes a new computer-aided diagnosis system for lung cancer diagnosis from chest computed tomography scans. The proposed system extracts two different kinds of features, namely, appearance features and shape features. For the appearance features, a Histogram of oriented gradients, a Multi-view analytical Local Binary Pattern, and a Markov Gibbs Random Field are developed to give a good description of the lung nodule texture, which is one of the main distinguishing characteristics between benign and malignant nodules. For the shape features, Multi-view Peripheral Sum Curvature Scale Space, Spherical Harmonics Expansion, and a group of some fundamental morphological features are implemented to describe the outer contour complexity of the nodules, which is main factor in lung nodule diagnosis. Each feature is fed into a stacked auto-encoder followed by a soft-max classifier to generate the initial malignancy probability. Finally, all these probabilities are combined together and fed to the last network to give the final diagnosis. The system is validated using 727 nodules which are subset from the Lung Image Database Consortium (LIDC) dataset. The system shows very high performance measures and achieves , , and for the accuracy, sensitivity, and specificity, respectively. This high performance shows the ability of the system to distinguish between the malignant and benign nodules precisely.
Highlights
According to the World Health Organization (WHO), cancer is the second leading cause of death globally after heart disease
This texture is correlated with the nodule growth rate, as high growth rate leads to a nonuniform texture for the nodule appearance, which results in variations in the Hounsfield unit (HU) values in the computer-assisted tomography (CT) images
The experimental results demonstrate that the presented non-invasive CAD system for the lung nodule diagnosis is promising and has achieved very high accuracy measures from a single computed tomography scan
Summary
According to the World Health Organization (WHO), cancer is the second leading cause of death globally after heart disease. The majority of patients arrive with late-stage disease (stages III and IV), and there are few viable treatment choices, resulting in significant fatality rates [7]. Another significant factor decreasing the mortality rate of this condition is the difficulty of lung cancer diagnosis, where extreme delays in recognizing cases of lung cancer may cause the deterioration of the patient’s condition. This explains the need for diagnostic and clinical protocols to be successful and incorporated promptly [8]. Detection of lung cancer enhances treatment effectiveness and boosts the five-year survival rate from 17.7% to 55.2% [9]
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