Machine learning-based lung and colon cancer detection using deep feature extraction and ensemble learning

Cancer is a fatal disease caused by combination of genetic diseases and variety biochemical abnormalities. Lung colon cancer have emerged as two the leading causes death disability in humans. The histopathological detection such malignancies usually most important component determining best course action. Early ailment on either front considerably decreases likelihood mortality. Machine learning deep techniques can be utilized to speed up detection, allowing researchers study large number patients much shorter amount time at lower cost. In this research work, we introduced hybrid ensemble feature extraction model efficiently identify lung cancer. It integrates with high-performance filtering for image datasets. evaluated (LC25000) According findings, our detect lung, colon, (lung colon) accuracy rates 99.05%, 100%, 99.30%, respectively. study’s findings show that proposed strategy outperforms existing models significantly. Thus, these could applicable clinics support doctor diagnosis cancers.