ArticleDiagnostics (Basel, Switzerland)2021
VGG19 Network Assisted Joint Segmentation and Classification of Lung Nodules in CT Images.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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33 citing papers in PubMed.
- Transformer-based and CNN-based models for clinically effective 2D and 3D pelvic bone segmentation in CT imaging.BMC musculoskeletal disorders · 2025Article
- Evaluation of Machine Learning Methods Developed for Prediction and Diagnosis of Pneumonia: A Systematic Review.Health science reports · 2025Review
- Dual-model approach for accurate chest disease detection using GViT and swin transformer V2.Scientific reports · 2025Article
- Explainable AI for lung cancer detection via a custom CNN on CT images.Scientific reports · 2025Article
- An artificial intelligence model utilizing endoscopic ultrasonography for differentiating small and micro gastric stromal tumors from gastric leiomyomas.BMC gastroenterology · 2025Article
- An enhanced YOLOv10 architecture for high-sensitivity and high-specificity lung cancer detection.Frontiers in oncology · 2025Article
- A lung nodule segmentation model based on the transformer with multiple thresholds and coordinate attention.Scientific reports · 2024Article
- Toward Robust Lung Cancer Diagnosis: Integrating Multiple CT Datasets, Curriculum Learning, and Explainable AI.Diagnostics (Basel, Switzerland) · 2024Article
- Differentiation of granulomatous nodules with lobulation and spiculation signs from solid lung adenocarcinomas using a CT deep learning model.BMC cancer · 2024Article
- Semantically redundant training data removal and deep model classification performance: A study with chest X-rays.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2024Article
- Overcoming the Challenge of Accurate Segmentation of Lung Nodules: A Multi-crop CNN Approach.Journal of imaging informatics in medicine · 2024Article
- Diagnosis model of early Pneumocystis jirovecii pneumonia based on convolutional neural network: a comparison with traditional PCR diagnostic method.BMC pulmonary medicine · 2024Article
- Pulmonary Nodule Classification Using a Multiview Residual Selective Kernel Network.Journal of imaging informatics in medicine · 2024Article
- An efficient combined intelligent system for segmentation and classification of lung cancer computed tomography images.PeerJ. Computer science · 2024Article
- EDTNet: A spatial aware attention-based transformer for the pulmonary nodule segmentation.PloS one · 2024Article
- Efficient Thorax Disease Classification and Localization Using DCNN and Chest X-ray Images.Diagnostics (Basel, Switzerland) · 2023Article
- MixNet-LD: An Automated Classification System for Multiple Lung Diseases Using Modified MixNet Model.Diagnostics (Basel, Switzerland) · 2023Article
- Article
- Deep-Learning Model of ResNet Combined with CBAM for Malignant-Benign Pulmonary Nodules Classification on Computed Tomography Images.Medicina (Kaunas, Lithuania) · 2023Article
- Brain Tumor Classification based on Improved Stacked Ensemble Deep Learning Methods.Asian Pacific journal of cancer prevention : APJCP · 2023Article
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7 authors.
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Abstract
Pulmonary nodule is one of the lung diseases and its early diagnosis and treatment are essential to cure the patient. This paper introduces a deep learning framework to support the automated detection of lung nodules in computed tomography (CT) images. The proposed framework employs VGG-SegNet supported nodule mining and pre-trained DL-based classification to support automated lung nodule detection. The classification of lung CT images is implemented using the attained deep features, and then these features are serially concatenated with the handcrafted features, such as the Grey Level Co-Occurrence Matrix (GLCM), Local-Binary-Pattern (LBP) and Pyramid Histogram of Oriented Gradients (PHOG) to enhance the disease detection accuracy. The images used for experiments are collected from the LIDC-IDRI and Lung-PET-CT-Dx datasets. The experimental results attained show that the VGG19 architecture with concatenated deep and handcrafted features can achieve an accuracy of 97.83% with the SVM-RBF classifier.
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