ArticleAnnals of translational medicine2019
Deep convolutional neural network Inception-v3 model for differential diagnosing of lymph node in cytological images: a pilot study.
Article in Annals of translational medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 3 of them syntheses that pooled it.
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Who cites it
18 citing papers in PubMed, 3 syntheses or guidelines pooled it, 66 citations in OpenAlex.
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- Artificial intelligence performance in detecting lymphoma from medical imaging: a systematic review and meta-analysis.BMC medical informatics and decision making · 2024Pooled it
- Deep learning applications in visual data for benign and malignant hematologic conditions: a systematic review and visual glossary.Haematologica · 2023Pooled it
- Deep Learning on Histopathological Images Enables Accurate Diagnosis of Common B-Cell and Hodgkin Lymphoma.Cancers · 2026Article
- Deep learning models for image classification of lymphoma: a pilot study in canine.The Journal of veterinary medical science · 2026Article
- Impact of Magnification, Image Type, and Number on Convolutional Neural Network Performance in Differentiating Canine Large Cell Lymphoma From Non-Lymphoma via Lymph Node Cytology.Veterinary clinical pathology · 2025Article
- Deep learning-based bacterial foraging optimization algorithm to improve digital mammography-based breast cancer detection.Scientific reports · 2025Article
- Deep Spectrogram Learning for Gunshot Classification: A Comparative Study of CNN Architectures and Time-Frequency Representations.Journal of imaging · 2025Article
- An Update on the Use of Artificial Intelligence in Digital Pathology for Oral Epithelial Dysplasia Research.Head and neck pathology · 2024Review
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- Fundus image classification using Inception V3 and ResNet-50 for the early diagnostics of fundus diseases.Frontiers in physiology · 2023Article
- Stroke risk prediction by color Doppler ultrasound of carotid artery-based deep learning using Inception V3 and VGG-16.Frontiers in neurology · 2023Article
- Tooth-Related Disease Detection System Based on Panoramic Images and Optimization Through Automation: Development Study.JMIR medical informatics · 2022Article
- COVID-19 Detection in Chest X-ray Images Using a New Channel Boosted CNN.Diagnostics (Basel, Switzerland) · 2022Article
- Research on the classification of lymphoma pathological images based on deep residual neural network.Technology and health care : official journal of the European Society for Engineering and Medicine · 2021Article
- Cytopathology 2.0: How Artificial Intelligence Is Redefining the Future of Cytopathology.Journal of cytologyReview
- Comprehensive benchmarking of deep learning architectures for multiclass histopathological classification of oral epithelial lesions.Journal of oral biology and craniofacial researchArticle
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9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn this study, we exploited the Inception-v3 deep convolutional neural network (DCNN) model to differentiate cervical lymphadenopathy using cytological images.
methodsA dataset of 80 cases was collected through the fine-needle aspiration (FNA) of enlarged cervical lymph nodes, which consisted of 20 cases of reactive lymphoid hyperplasia, 24 cases of non-Hodgkin's lymphoma (NHL), 16 cases of squamous cell carcinoma (SCC), and 20 cases of adenocarcinoma. The images were cropped into fragmented images and divided into a training dataset and a test dataset. Inception-v3 was trained to make differential diagnoses and then tested. The features of misdiagnosed images were further analysed to discover the features that may influence the diagnostic efficiency of such a DCNN.
resultsA total of 742 original images were derived from the cases, from which a total of 7,934 fragmented images were cropped. The classification accuracies for the original images of reactive lymphoid hyperplasia, NHL, SCC and adenocarcinoma were 88.46%, 80.77%, 89.29% and 100%, respectively. The total accuracy on the test dataset was 89.62%. Three fragmented images of reactive lymphoid hyperplasia and three fragmented images of SCC were misclassified as NHL. Three fragmented images of NHL were misclassified as reactive lymphoid hyperplasia, one was misclassified as SCC, and one was misclassified as adenocarcinoma.
conclusionsIn summary, after training with a large dataset, the Inception-v3 DCNN model showed great potential in facilitating the diagnosis of cervical lymphadenopathy using cytological images. Analysis of the misdiagnosed cases revealed that NHL was the most challenging cytology type for DCNN to differentiate.
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