مختصر البحث:
Abstract
Introduction:
Lung cancer is one of the deadliest type of cancer for both men and women, all around the world, which is the highest mortality rates in all cancers. Hence early detection of lung cancer is the most promising way to improve …
Abstract
Introduction:
Lung cancer is one of the deadliest type of cancer for both men and women, all around the world, which is the highest mortality rates in all cancers. Hence early detection of lung cancer is the most promising way to improve the patient's chances of survival, and is one of the best possible clinical outcomes for patients. Chest X-ray (CXR) is the most common test used as a primary diagnostic tool for a variety of clinical conditions, which includes at least one-third of all radiology tests. Many diseases, such as lung cancer, can be diagnosed in the early stages by various approaches such as periodic health screenings. Lung cancer can appear by observing a single nodule in CXR images, thus recognizing pulmonary nodules can provide a significant impact on early detection of lung cancer.
Method analysis:
Chest X-ray is an effective tool for detecting lung tumors, but interpretation of these images are difficult due to the anatomical structure of the image. An experienced radiologist sometimes faces a disturbance in the X-ray image. Different computer-assisted diagnostic methods have been proposed to help the radiologist find disturbances of x-ray images, but none of them have a complete effect due to the complexity of the anatomical structure in the image and the tiny nodules. In this study, various methods for finding a tumor from chest radiographs from document databases around the world have been investigated. These methods often are tested on standard images databases, such as the famous JSRT database. Most methods in articles are categorized in some stages: preprocessing, main processing involves lung field detection, feature extraction (with different methods) to find candidate nodules, and then reducing the false nodule candidates (FP). Finally, a classification method estimate real nodules. A brief description of each of these methods is given and listed in a table in full manuscript.
Conclusion:
Large areas of Chest X-ray images, like bones and organs in the image, also excessive amount of noise and tissue-to-tissue interactions with the lung and its tumors are shown in CXR image. The studies have shown that these challenges, in addition lack of in-depth methods, cause false nodules to be detected, thereby reducing evaluation criteria values. Various methods have been proposed and reported various criteria such as accuracy, precision, specificity, sensitivity, area under curve (AUC) and so on. Nowadays, deep neural network approach have been used to solve this problem, but all of the presented methods have their own unique advantages and disadvantages, and none of them has complete results due to the challenges posed and tiny nodules. We wish that new proposed methods can be used to solve this problem using the high abilities.
Keywords: computer-aided diagnosis (CAD), chest X-ray image, lung nodule, feature extraction, classification.
