Identifying Lung Cancer Using CT Scan Images Based on Artificial Intelligence

Abstract

Lung cancer is among the leading cause of death among men and women. Early detection of lung cancers can increase the possibility of survival amongst patients. The preferred 5-years survival rate for lung most cancers sufferers will increase from 16% to 50% if the disease is detected on time. Computerized tomography (CT) is frequently used for diagnosis and is more efficient than X-ray. However, the images need to be reviewed by a qualified physician who specializes in interpreting the CT scan. This may lead to misinterpretation and conflicting reports among physicians. Therefore, a lung cancer detection system that uses image processing methods to categorize lung cancer in CT images will be more consistent and precise. This paper presents a lung cancer detection system using the Artificial Intelligence (AI) method. The study uses Median, Gaussian, and Watershed segments to reduce noisy and shredded CT images. Then, the Weight Optimization Neural Network method was used to improve accuracy and reduce the computational time. The results were compared with previous works and shows higher accuracy and shorter computational time.

Spatial domain noise removal filtering for low-resolution digital images

“…The potential of digital image processing concepts was only realized through the combination of these two developments; the first development is significant because earlier computers could not handle the complexity and processing power required for digital image representation in a computer, and as a result ,processing a digital image required a sufficient amount of space for the image handling utilization to run and carry out the most fundamental operations on the recently introduced digital image [3]. Computerized tomography (CT), for example Lung cancer [4], and astronomical observations are two major applications for digital imaging using different electromagnetic (EM) fields like gamma-ray, while X-ray imaging is also widely used in industry and in other fields, such as astronomy and medical diagnostics. Additionally, the ultra violet (UV) band is used for industrial inspection, lithography, microscopy, biological imaging and astronomical investigations.…”