Lung Cancer Classification and Prediction Using Machine Learning and Image Processing
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BioMed Research International
Abstract
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.
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Research Article