Automatic Lung Cancer Prediction from Chest X-ray Images Using Deep Learning Approach

TL;DR

This paper explores using DenseNet-121 with transfer learning to classify lung cancer from chest X-ray images, achieving promising accuracy and providing heatmaps for nodule localization, addressing small dataset challenges.

Contribution

The study applies DenseNet-121 with transfer learning to lung cancer detection from X-rays, demonstrating effective classification and nodule localization despite limited data.

Findings

Achieved around 74.4% accuracy in lung cancer classification.

Provided heatmaps for lung nodule localization.

Addressed small dataset problem effectively.

Abstract

Since, cancer is curable when diagnosed at an early stage, lung cancer screening plays an important role in preventive care. Although both low dose computed tomography (LDCT) and computed tomography (CT) scans provide more medical information than normal chest x-rays, there is very limited access to these technologies in rural areas. Recently, there is a trend in using computer-aided diagnosis (CADx) to assist in screening and diagnosing of cancer from biomedical images. In this study, the 121-layer convolutional neural network also known as DenseNet-121 by G. Huang et. al., along with the transfer learning scheme was explored as a means to classify lung cancer using chest X-ray images. The model was trained on a lung nodules dataset before training on the lung cancer dataset to alleviate the problem of a small dataset. The proposed model yields 74.43$\pm$6.01\% of mean accuracy, 74.96$\pm$9.85\% of mean specificity, and 74.68$\pm$15.33\% of mean sensitivity. The proposed model also provides a heatmap for identifying the location of the lung nodule. These findings are promising for further development of chest x-ray-based lung cancer diagnosis using the deep learning approach. Moreover, these findings solve the problem of small dataset.