A Deep Learning Algorithm for Lung Cancer Detection Using EfficientNet-B3

Abstract

Lung carcinoma is one of the main causes of deaths over the whole world, causing a global burden of morbidity and mortality. Detecting lung tumors at their early stages can help reducing the risk of having lung cancer. This paper proposes a deep learning algorithm using EfficientNet B3 for lung cancer detection. The purpose is to improve detection accuracy highlighting potential to revolutionize the field of medical imaging and improve patient care. The proposed approach is build based on EfficientNet B3 model to classify four different types of lung cancer. The approach used CT scan images labeled into Normal, Squamous.cell.carcinoma, Large.cell.carcinoma, and Adenocarcinoma for the purpose of lung cancer detection. The results showed that the proposed model provided an improvement rate of 2.13% compared with the best-trained classifier with accuracy of 96%. This model can be generalized to improve lung cancer detection. The finding of deep neural networks, particularly EfficientNet B3, in supporting the diagnosis and detection of the lung disease, particularly in its early times.