Lung And Colon Cancer Detection Using Weighted Average Ensemble Transfer Learning

Abstract

Cancer is a deadly disease that is caused by metabolic abnormalities or a confluence of inherited diseases. Lung and colon cancer are considered as two of the most widely spread causes of disability and death in humans today. In the process of determining the best course of therapy for patients, the histological diagnosis of these tumors is the most important aspect. Diagnosis of the cancer in an early stage before spreading even more in the body will reduce the risk of death greatly on either front. Through utilizing the machine learning and deep learning models, this type of cancer diagnosis can be sped, providing researchers a cost-effective way to analyze a larger number of patients in much less time. In this study, we propose an ensemble transfer learning model to rapidly diagnose lung and colon cancer. Through utilizing multiple transfer learning models and ensemble them for a better performance. The proposed model uses the lung and colon histology (LC25000) dataset, Our models has an accuracy for each of, MobileNet V1, Inception V3, and VGG16 98.32%, 98%, and 96.93% for lung and colon cancer detection, respectively, while our ensemble model has an accuracy of 99.44%. This study’s findings indicate that our proposed method outperforms existing models therefor it could be used in clinics to assist medical personnel with the detection of lung and colon cancer.