Machine learning-driven multistage classification for lung lesion detection

Multi-stage classification for lung lesion detection on CT scan images employs a hierarchical approach, involving sequential stages for accurate identification and classification. This methodology integrates medical image processing techniques, including segmentation, feature extraction, selection, and classification, to enhance the detection performance and reliability of lung lesion diagnosis. Medical imaging, particularly medical image processing, is rapidly advancing and transforming various aspects of healthcare, including prevention, diagnosis, and treatment. In lung cancer diagnosis, computed tomography (CT) scans play a crucial role. Accurate identification of masses is essential, as misdiagnosis can lead to incorrect treatments. Detecting and delineating masses within lung tissue pose critical challenges in diagnosis. In this work, a segmentation system in image processing techniques has been applied for detection purposes. Particularly, the use and validation of a novel lung cancer detection algorithm have been presented through simulation. This has been performed employing CT images based on multilevel thresholding. The proposed technique consists of segmentation, feature extraction, and feature selection and classification. More in detail, the features with useful information are selected after featuring extraction. Eventually, the output image of the lung cancer is obtained with 96.3% accuracy and 87.25%. The purpose of feature extraction applying the proposed approach is to transform the raw data into a more usable form for subsequent statistical processing. Future steps will involve employing the current feature extraction method to achieve more accurate resulting images including further details available to machine vision systems to recognise objects in lung CT scan images.