Modernizing Cancer Diagnosis with an Intelligent Model for Lung and Colon Cancer

Abstract

Cancer is a lethal condition resulting from a mix of genetic mutations and several metabolic irregularities. Lung and Colon Cancer (LCC) are the primary reasons for mortality and impairment in humans. Identifying these malignancies by histopathology is often crucial for deciding the most appropriate therapy. Early discovery of the disease on the side significantly reduces the chances of death. Machine Learning (ML) methods accelerate cancer diagnosis, enabling researchers to analyze more individuals in a smaller time frame and at a reduced fee. The research presented a hybrid ensemble-based feature-extracting system to detect LCC effectively. It combines advanced feature extraction and ensemble-based learning with efficient filtration for datasets of LCC images. The system is assessed using histopathology datasets for the LCC. The research indicates that the hybrid system can accurately diagnose the lungs and colon. Therefore, these models might be used in clinical settings to assist doctors in diagnosing malignancies.