The Progress on Lung Computed Tomography Imaging Signs: A Review

Abstract

Lung cancer is the highest-mortality cancer with the largest number of patients in the world. Early screening and diagnosis of lung cancer by CT imaging is of great significance to improve the cure rate of lung cancer. CT signs mean the information of comprehensive manifestations of diseases at different pathological stages and levels. Automatic analysis of CT images outputs the locations and sizes of lesion regions which can help radiologists to make a credible diagnosis and effectively improve the speed and accuracy of clinical diagnosis. In this paper, we first review the domestic and foreign research progress of lung CT signs, summarize a generic structure for expressing the implementation process of existing methods, and systematically describe the signs research based on the traditional machine learning method and deep learning method. Furthermore, we provide a systematic summary and comparative analysis of the existing methods. Finally, we point out the challenges ahead and discuss the directions for improvement of future work, providing reference for scholars in related fields.

Epidemiological and Cyto-Morphological Spectrum of Pulmonary Lesion by Ct Guided Fnac

“…[4] By the virtue of technological advancement, FNAC (Fine needle aspiration cytology) is now-a-days administered with a computed tomography (CT) scan which has made this procedure more accurate, safe, quick and dependable procedure for detecting pulmonary lesions. [4,5] Mondal et al, reported that the FNAC not only differentiates benign from malignant lesions, but it also helps in lung cancer tumor typing, enabling for the immediate commencement of therapeutic intervention such as chemotherapy or surgery. [4] This whole procedure involves a teamwork approach, in which the presence of pathological staff during the procedure may improve the overall sensitivity and accuracy of tumor typing by reducing the number of needle passes and thus easing patient pain or trauma.…”