Lung and Colon Cancer Classification Using Medical Imaging Through Transfer Learning

Abstract

Carcinomas of the lung and colon are among the most common sources of invasive cancer and are the two most common causes of cancer deaths in worldwide. Detecting and treating cancer at an initial stage can diminish death rates worldwide. At present, transfer learning is the very extensively used, compelling and successful imaging approach for the recognition of lung and colon cancer. As part of this study, 10 CNN architectures are analyzed to find ascertain which model provides the best performance for detecting colon and lung cancer with the minimum amount of data loss and completion time: VGG16, VGG19, MobileNet, MobileNetV2, InceptionV3, ResNet50, ResNet50V2, ResNet101, DenseNet201 and Xception. Performance, data loss, and completion time are compared using an evaluation matrix. MobileNet performs with the highest accuracy, VGG19 performs with the second-highest accuracy, and VGG16 performs with third highest accuracy. The dataset contains 25,000 images. MobileNet achieved the best 99.90% training accuracy, 99.88% validation accuracy, and 99.58% test accuracy. It takes the lowest completion time, 45 seconds per epoch, with 0.3323 % of data loss and gives the highest result with the least epoch. Based on image processing and transfer learning, the recommended method yields the highest accuracy, the least completion time, and the least data loss.