A Critical Analysis on Early Stage of Lungs and Colon Cancer Detection Using CNN Algorithm

One of the most dangerous problems that people experience globally is lung and colon tumor that has spread rapidly to become a normal medical challenging problem. It is essential to achieve an accurate and preliminary detection in order to minimize the hike of death. The complexity of the task entirely relies on the histopathologists experience. The analysis of the histopathology images of lung and colon cancer is crucial because early and accurate diagnosis of cancer histology is actually needed and since the treatment of cancer is aimed on the kind of histology, genetic profile and stage of the illness. Histologists may even endanger the patient’s life. In recent times popularity of deep learning increase and recognized in the diagnosis and treatment of medical imaging. In order to identify lung and colon cancer using histopathological image and more effective augmentation techniques, this research involves studying and modifying the current CNN model. In the past few years, CNN has reported amazing progress in the fields of CT, MRI, and ultrasound. A novel technique that combines the advantages of two imaging modalities, 3D CT scan and histopathological image processing—to produce results that are more accurate and dependable. They are trained on the LC25000 dataset. CNN are the most preferred approach of deep learning algorithm for the advanced detection of lung and colon cancer. The level of accuracy offered by CNN in this regard may help to reduce the frequency and improve evaluation and quality