A Review of Automatic Nodule Detection Algorithms of Lung Cancer in CT-Scan Images

Abd Al Rahman, Zahraa Hussain; Mohammed, Elham

doi:10.63463/kjes1046

	A Review of Automatic Nodule Detection Algorithms of Lung Cancer in CT-Scan Images
Kerbala Journal for Engineering Sciences (KJES)
Article 3, Volume 2, Issue 2, 2022, Pages 108-128 PDF (525.76 K)
Document Type: Review Article
DOI: 10.63463/kjes1046
Authors
Zahraa Hussain Abd Al Rahman^* ; Elham Mohammed
Department of Information Technology, College of Computer Science and Information Technology, University of Kerbala, Karbala, Iraq
Abstract
Lung cancer has repeatedly surfaced as among the most fatal diseases that humanity has ever known. It is also one of the highest frequent malignancies and one of the leading causes of mortality. Lung cancer cases are quickly expanding. The disorder has a desire to keep asymptomatic in its initial phases, the ability to manifest is exceedingly difficult. As a result, early tumor identification is critical in healing. The sooner a patient is diagnosed, the better his or her prospects of healing and survival. In order to effectively diagnose the condition, technology is crucial. Based on their observations, several researchers have come up with solutions. In the latest years, a number of computer-aided diagnostic (CAD) procedures and systems have been proposed, implemented, and produced in order to use digital transmission to tackle this issue. Such algorithms employ a variety of machine learning and deep learning approaches, along with multiple methodologies based on image processing-based approaches for predicting cancer malignant levels. The purpose of this research is to find, compare, and evaluate a variety of image categorization, semantic segmentation, and other methodologies for categorizing and identifying lung cancer in its early phases.
Keywords
CAD System; Lung Cancer; Machine learning

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