article · BioMed Research International
Lung cancer presents a severe medical challenge, but early detection allows for treatment. Applying machine learning and image processing offers an approach to identify affected regions in the lungs. An investigation evaluated these techniques using a dataset of 83 CT scans collected from 70 distinct patients. During the initial preprocessing phase, a geometric mean filter was applied to enhance picture quality through noise reduction. The K-means clustering technique was subsequently utilised to segment the scans and locate the damaged areas of interest. For the classification and prediction stage, multiple machine learning methods were tested, including artificial neural networks, k-nearest neighbours, and random forest models. Comparative assessment revealed that the artificial neural network model delivered more accurate results for predicting lung cancer than the alternative algorithms evaluated.
Lung cancer remains a potentially lethal condition where catching the disease early is critical for effective treatment. Demonstrating that image processing combined with neural network models can accurately identify damaged lung tissue on CT scans helps advance computer-aided diagnostics, potentially assisting medical professionals in detecting suspicious lesions more reliably and earlier in the clinical workflow.
The approach could support diagnostic software tools designed for medical imaging specialists and healthcare providers interpreting CT scans. Given that the pipeline was tested on an experimental dataset of 83 scans across three standard machine learning classifiers, this represents early-stage exploratory research. Considerable further validation on larger cohorts and integration into clinical workflows would be necessary before real-world diagnostic deployment.
AI-generated from the published abstract. Always read the original work before citing.
Lung cancer is a potentially lethal illness. Cancer detection continues to be a challenge for medical professionals. The true cause of cancer and its complete treatment have still not been discovered. Cancer that is caught early enough can be treated. Image processing methods such as noise reduction, feature extraction, identification of damaged regions, and maybe a comparison with data on the medical history of lung cancer are used to locate portions of the lung that have been impacted by cancer. This research shows an accurate classification and prediction of lung cancer using technology that is enabled by machine learning and image processing. To begin, photos need to be gathered. In the experimental investigation, 83 CT scans from 70 distinct patients were utilized as the dataset. The geometric mean filter is used during picture preprocessing. As a consequence, image quality is enhanced. The K ‐means technique is then used to segment the images. The part of the image may be found using this segmentation. Then, classification methods using machine learning are used. For the classification, ANN, KNN, and RF are some of the machine learning techniques that were used. It is found that the ANN model is producing more accurate results for predicting lung cancer.
This page summarises published work. The authoritative version sits with the publisher.
DOI: 10.1155/2022/1755460
Is something wrong with this record? Report it or request removal.
Discussion
Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.
No discussion yet. Open the first thread.
New to MARATTO™? Create a free account.
