Computer aided lung cancer diagnosis with deep learning algorithms

By W. Sun, Bin Zheng, Wei Qian. SPIE Medical Imaging, 2016

304 citations recorded by the source corpus.

Deep learning is considered as a popular and powerful method in pattern recognition and classification. However, there are not many deep structured applications used in medical imaging diagnosis area, because large dataset is not always available for medical images. In this study we tested the feasibility of using deep learning algorithms for lung cancer diagnosis with the cases from Lung Image Database Consortium (LIDC) database. The nodules on each computed tomography (CT) slice were segmented according to marks provided by the radiologists. After down sampling and rotating we acquired 174412 samples with 52 by 52 pixel each and the corresponding truth files. Three deep learning algorithms were designed and implemented, including Convolutional Neural Network (CNN), Deep Belief Networks (DBNs), Stacked Denoising Autoencoder (SDAE). To compare the performance of deep learning algorithms with traditional computer aided diagnosis (CADx) system, we designed a scheme with 28 image features and support vector machine. The accuracies of CNN, DBNs, and SDAE are 0.7976, 0.8119, and 0.7929, respectively; the accuracy of our designed traditional CADx is 0.7940, which is slightly lower than CNN and DBNs. We also noticed that the mislabeled nodules using DBNs are 4% larger than using traditional CADx, this might be resulting from down sampling process lost some size information of the nodules.

Subfields: Artificial Intelligence, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/7cea8cc667db38d1665bcaaf681cad6b92cb6757

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In Artificial Intelligence

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

A review on deep learning in medical image analysis (2021, background): “Deep learning is not a solitary method, yet rather a class of calculations and geographies that can be applied to a wide assortment of issues [102, 103].”
The present and future of deep learning in radiology. (2019, methodology): “CNN has been used for both medical image characterization [48,49] and segmentation [50,51].”
Visualizing Convolutional Neural Networks to Improve Decision Support for Skin Lesion Classification (2018, methodology): “In medicine, deep learning is used as a tool to assist professionals of various subfields in their diagnoses, such as histopathology [11], oncology [1,4,17], pulmonology [7,15], etc1.”
Deep Learning and Medical Diagnosis: A Review of Literature (2018, unknown): “[46] CNN, DBN, SDAE CT Lung cancer diagnosis; highest accuracy was achieved with DBN (0.”
Deep learning for lung Cancer detection and classification (2020, methodology): “Wenqing et al. [30] have used three deep learning algorithms such as CNN, Deep Belief Networks (DBNs) and Stacked Denoising Autoencoder (SDAE) for lung cancer classification.”
Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network (2020, background): “With the availability of enough annotated images, deep learning approaches [1,3,29] have demonstrated their superiority over the classical machine learning approaches.”
Hybrid deep learning for detecting lung diseases from X-ray images (2020, unknown): “Many researchers have done investigations to relate machine learning schemes for prediction of Xray image diagnostic information [5-7].”