International Journal of Advances in Electrical Engineering
2024, Vol. 5, Issue 2, Part A
Statistic and morphological image processing for automated lung cancer identification
Author(s): Muhammad D Hassan and Suad Kakil Ahmed
Abstract: Among the population of Jordan, lung cancer is the second most usually diagnosed form of the disease. Over the last several years, there has been an increasing amount of data that demonstrates that early identification of lung cancer may allow for more rapid treatment action. These pieces of evidence have been accumulated over the course of the last few years. Screening programs for lung cancer have been established all over the globe as a result of this, which has supplied the motivation for their implementation. For the goal of finding regions of lung cancer, a computer-aided detection (CAD) system is presented within the scope of this investigation. The objective of this system is to make use of computed tomography (CT) pictures. It is now being deployed as a "second reader" in order to assist radiologists in concentrating their attention on areas that may be overlooked during visual interpretation. This is being done in order to help radiologists concentrate their attention on relevant regions. First, the CT images are thresholder, then the sections that are created are labeled, and finally, certain diagnostic characteristics of each location are obtained for further analysis and interpretation. It is via this process that segmentation is achieved. The proposed computer-aided design (CAD) system is comprised of these three major processes, which are listed and described below. The study is taught, evaluated, and verified with the use of photographs gathered from forty-five different patients. The findings that were acquired are an exact match for the diagnosis that was made by the radiologist throughout the process of identifying the defective regions and quantitatively measuring their size, position, and boundaries, in addition to exhibiting their other diagnostic features. Additionally, the suggested approach is able to identify locations that have been incorrectly categorized.
DOI: 10.22271/27084574.2024.v5.i2a.74
Pages: 67-73 | Views: 261 | Downloads: 87
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How to cite this article:
Muhammad D Hassan, Suad Kakil Ahmed. Statistic and morphological image processing for automated lung cancer identification. Int J Adv Electr Eng 2024;5(2):67-73. DOI: 10.22271/27084574.2024.v5.i2a.74
