Image Processing in Healthcare: Lung Cancer Detection

Abstract

Lung cancer is still a significant worldwide health issue despite medical advancements and rising public awareness of the risks of smoking, which has led to the ongoing development of novel diagnostic and treatment approaches to lessen the burden of this illness. It highlights the significance of research and development. If lung cancer is found and diagnosed early, it is significantly more curable and has a higher chance of survival. Medical imaging methods including computed tomography (CT), magnetic resonance imaging (MRI), and X-rays can be used to identify and diagnose lung cancer. However, because human mistake is widespread, manually interpreting these photographs involves a substantial danger of being deceptive.

The accuracy and efficacy of picture interpretation in medicine might be increased with the use of image processing tools. Recently, several image processing methods for lung cancer diagnosis have been developed, including segmentation, feature extraction, and classification. Small nodules may be recognised, their size and form measured, their growth over time tracked, and their cancerous ness assessed using these techniques.

In addition to increasing detection accuracy, the application of CNNs in the detection of lung cancer has also made it possible to automate the process of image analysis for medical purposes, enabling quicker and more precise diagnosis as well as more efficient treatment. In the end, it could result in better patient results. Because they can accurately categorise nodules and automatically learn characteristics from medical pictures, CNNs are a promising tool for lung cancer screening. Furthermore, when pretrained CNNs are enhanced for lung nodule classification, transfer learning has promising outcomes in enhancing the precision of lung cancer detection.

CNNs are enhanced for lung nodule classification, transfer learning has promising outcomes in enhancing the precision of lung cancer detection.

To enhance the use of imaging techniques for diagnosing lung cancer, several issues must be resolved. The fact that nodule appearance is widely changeable is one of the key issues, which might lower the accuracy of nodule identification and categorization. The quality of medical pictures can also be impacted by patient movement, image artefacts, and radiation exposure, which has a significant influence on how well image processing algorithms work.

In this article, we provide a novel segmentation, feature extraction, and classification image processing approach based on CNN for the detection of lung cancer. With our method, we use transfer learning to improve a CNN that has already been trained to categorise lung nodules, which solves the issue of limited training data. We evaluated the performance of our proposed method using a publicly available dataset and compared it to that of alternative image processing techniques. Our results show that the proposed method performs more accurately and sensitively than existing approaches, showing its potential to improve lung cancer diagnosis.