ArticleJournal of biomedical optics2017
Combining deep learning and coherent anti-Stokes Raman scattering imaging for automated differential diagnosis of lung cancer.
Article in Journal of biomedical optics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 2 of them syntheses that pooled it.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
28 citing papers in PubMed, 2 syntheses or guidelines pooled it, 103 citations in OpenAlex.
- Raman spectroscopy in the detection and diagnosis of lung cancer: a meta-analysis.Lasers in medical science · 2025Pooled it
- The efficacy of Raman spectroscopy in lung cancer diagnosis: the first diagnostic meta-analysis.Lasers in medical science · 2022Pooled it
- AI-Assisted Coherent Raman Scattering Microscopy for Clinical Translation.Chemical & biomedical imaging · 2026Review
- Non-invasive Imaging Techniques: From Histology to In-vivo Imaging.Recent results in cancer research. Fortschritte der Krebsforschung. Progres dans les recherches sur le cancer · 2026Review
- Advancements and future trends in machine learning for lung cancer: a comprehensive bibliometric analysis.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Review
- Integrating spatial and chemical information enhances differentiation of non-alcoholic steatohepatitis states in Raman imaging.Scientific reports · 2025Article
- Detection of malignant lung tumors using stimulated Raman histology and convolutional neural networks.Journal of thoracic disease · 2025Article
- Optical sensor for fast and accurate lung cancer detection with tissue autofluorescence and diffuse reflectance spectroscopy.Thoracic cancer · 2025Article
- Utility of Raman Spectroscopy in Pulmonary Medicine.Advances in respiratory medicine · 2024Review
- Early classification of Alzheimer's disease phenotype based on hippocampal electrophysiology in the TgF344-AD rat model.iScience · 2023Article
- Multimodal attention-based deep learning for Alzheimer's disease diagnosis.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Review
- Optical clearing and testing of lung tissue using inhalation aerosols: prospects for monitoring the action of viral infections.Biophysical reviews · 2022Review
- Deep learning-based image processing in optical microscopy.Biophysical reviews · 2022Review
- Artificial intelligence for localization of the acute ischemic stroke by non-contrast computed tomography.PloS one · 2022Article
- Special Series Guest Editorial: Artificial Intelligence and Machine Learning in Biomedical Optics.Journal of biomedical optics · 2021Article
- Recent advances in sensitive surface-enhanced Raman scattering-based lateral flow assay platforms for point-of-care diagnostics of infectious diseases.Sensors and actuators. B, Chemical · 2021Article
- Multimodal deep learning models for early detection of Alzheimer's disease stage.Scientific reports · 2021Article
- Decoding Optical Data with Machine Learning.Laser & photonics reviews · 2021Article
- Construction of the Reverse Resource Recovery System of e-Waste Based on DLRNN.Computational intelligence and neuroscience · 2021Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
Abstract
Lung cancer is the most prevalent type of cancer and the leading cause of cancer-related deaths worldwide. Coherent anti-Stokes Raman scattering (CARS) is capable of providing cellular-level images and resolving pathologically related features on human lung tissues. However, conventional means of analyzing CARS images requires extensive image processing, feature engineering, and human intervention. This study demonstrates the feasibility of applying a deep learning algorithm to automatically differentiate normal and cancerous lung tissue images acquired by CARS. We leverage the features learned by pretrained deep neural networks and retrain the model using CARS images as the input. We achieve 89.2% accuracy in classifying normal, small-cell carcinoma, adenocarcinoma, and squamous cell carcinoma lung images. This computational method is a step toward on-the-spot diagnosis of lung cancer and can be further strengthened by the efforts aimed at miniaturizing the CARS technique for fiber-based microendoscopic imaging.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.