Transfer Learning by Cascaded Network to identify and classify lung nodules for cancer detection

TL;DR

This paper presents a cascaded transfer learning architecture that accurately segments and classifies lung nodules in CT images, achieving high accuracy and outperforming existing methods in early lung cancer detection.

Contribution

It introduces a novel cascaded architecture with transfer learning for improved lung nodule segmentation and classification, reducing complexity and enhancing accuracy.

Findings

Achieved 95.67% AUC in segmentation.

Classified nodules with 97.96% accuracy.

Outperformed conventional and deep architectures.

Abstract

Lung cancer is one of the most deadly diseases in the world. Detecting such tumors at an early stage can be a tedious task. Existing deep learning architecture for lung nodule identification used complex architecture with large number of parameters. This study developed a cascaded architecture which can accurately segment and classify the benign or malignant lung nodules on computed tomography (CT) images. The main contribution of this study is to introduce a segmentation network where the first stage trained on a public data set can help to recognize the images which included a nodule from any data set by means of transfer learning. And the segmentation of a nodule improves the second stage to classify the nodules into benign and malignant. The proposed architecture outperformed the conventional methods with an area under curve value of 95.67\%. The experimental results showed that the classification accuracy of 97.96\% of our proposed architecture outperformed other simple and complex architectures in classifying lung nodules for lung cancer detection.