The application and comparison of Deep Learning models for the prediction of chest cancer prognosis

Abstract

Lung cancers of all varieties, esophageal cancers, and cancers of the mediastinum (the area between the lungs), pleura (the membrane lining the chest cavity and surrounding the lungs), trachea, thymus gland, and heart are all classified as chest cancers, often known as thoracic cancers. Chest cancer can also spread from cancers that start in other places of the body. Chest pain is one of the usual signs of chest cancer, including hemoptysis or a cough that produces blood. Also, Coughing that hurts or a cough that does not go away is a sign of chest cancer. Mesothelioma, a cancer that begins in the lining of the chest or abdomen, frequently affects the lungs and other thoracic organs and tissues, which has prompted us to continue with this disease so that this research will aid in early detection. Chest X-rays and computed tomography (CT) pictures are the two diagnostic techniques that are most frequently utilized for these disorders. This study suggests a multiclassification deep learning model for detecting chest cancer using a dataset of chest CT-Scan pictures. While a chest CT scan is helpful even before symptoms show up and precisely detects the aberrant features that are found in images, a chest X-ray is less effective in the early stages of the disease.Furthermore, employing these kinds of photos will improve classification precision. To the best of our knowledge, no deep learning model in the literature can choose between these disorders. The current work considers the effectiveness of three architectures— CNN, ResNet50, and DenseNet121—. A thorough assessment of various deep learning architectures is performed using publicly available digital CT datasets with four classifications (Adenocarcinoma, Large Cell Carcinoma, Squamous Cell Carcinoma, and Normal). The study’s findings revealed that the DenseNet121 model performs better than the three other suggested models. CNN demonstrated 56.19% accuracy, whereas ResNet50 demonstrated 56.51% accuracy. The DenseNet121 model demonstrated 71.74% accuracy (ACC). We intend to investigate further deep learning models with large datasets.