Automatic Lung Cancer Prediction from Chest X-ray Images Using Deep Learning Approach
This addresses early lung cancer screening in rural areas with limited access to advanced imaging, though it is incremental as it applies an existing deep learning method to a specific medical dataset.
The study tackled lung cancer prediction from chest X-ray images using a DenseNet-121 model with transfer learning, achieving a mean accuracy of 74.43±6.01%, specificity of 74.96±9.85%, and sensitivity of 74.68±15.33%, and providing heatmaps for nodule localization.
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.