ArticleElectronics2022
A Hybrid Framework for Lung Cancer Classification.
Article in Electronics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed.
- The Differential Diagnostic Value of Radiomics Signatures Between Single-Nodule Pulmonary Metastases and Second Primary Lung Cancer in Patients with Colorectal Cancer.Technology in cancer research & treatmentTrial
- Compact neural network algorithm for electrocardiogram classification.Physical and engineering sciences in medicine · 2026Article
- Modified ShuffleNet trained on gradient pattern and shape-based features for lung cancer classification with improved M-SegNet segmentation.Scientific reports · 2026Article
- CIRCA: comprehensible online system in support of chest X-rays-based screening by COVID-19 example.Scientific reports · 2025Article
- Comparative analysis of supervised and self-supervised learning with small and imbalanced medical imaging datasets.Scientific reports · 2025Article
- Leveraging an ensemble of EfficientNetV1 and EfficientNetV2 models for classification and interpretation of breast cancer histopathology images.Scientific reports · 2025Article
- Application of the joint clustering algorithm based on Gaussian kernels and differential privacy in lung cancer identification.Scientific reports · 2025Article
- Deep learning based on intratumoral heterogeneity predicts histopathologic grade of hepatocellular carcinoma.BMC cancer · 2025Article
- Mixed-Supervised Learning for Cell Classification.Sensors (Basel, Switzerland) · 2025Article
- Fusion-driven semi-supervised learning-based lung nodules classification with dual-discriminator and dual-generator generative adversarial network.BMC medical informatics and decision making · 2024Article
- Greylag goose optimization and multilayer perceptron for enhancing lung cancer classification.Scientific reports · 2024Article
- Intelligent vehicle lateral control strategy research based on feedforward + predictive LQR algorithm with GA optimisation and PID compensation.Scientific reports · 2024Article
- Automated Lung Cancer Diagnosis Applying Butterworth Filtering, Bi-Level Feature Extraction, and Sparce Convolutional Neural Network to Luna 16 CT Images.Journal of imaging · 2024Article
- Detecting Parkinson's disease from shoe-mounted accelerometer sensors using convolutional neural networks optimized with modified metaheuristics.PeerJ. Computer science · 2024Article
- Uncertainty Estimation in Unsupervised MR-CT Synthesis of Scoliotic Spines.IEEE open journal of engineering in medicine and biology · 2024Article
- UKSSL: Underlying Knowledge Based Semi-Supervised Learning for Medical Image Classification.IEEE open journal of engineering in medicine and biology · 2024Article
- Applying Recurrent Neural Networks for Anomaly Detection in Electrocardiogram Sensor Data.Sensors (Basel, Switzerland) · 2023Article
- Unsupervised Low-Light Image Enhancement Based on Generative Adversarial Network.Entropy (Basel, Switzerland) · 2023Article
- DLBCNet: A Deep Learning Network for Classifying Blood Cells.Big data and cognitive computing · 2023Article
- Machine Learning for Lung Cancer Diagnosis, Treatment, and Prognosis.Genomics, proteomics & bioinformatics · 2022Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer is the second leading cause of death worldwide, and the death rate of lung cancer is much higher than other types of cancers. In recent years, numerous novel computer-aided diagnostic techniques with deep learning have been designed to detect lung cancer in early stages. However, deep learning models are easy to overfit, and the overfitting problem always causes lower performance. To solve this problem of lung cancer classification tasks, we proposed a hybrid framework called LCGANT. Specifically, our framework contains two main parts. The first part is a lung cancer deep convolutional GAN (LCGAN) to generate synthetic lung cancer images. The second part is a regularization enhanced transfer learning model called VGG-DF to classify lung cancer images into three classes. Our framework achieves a result of 99.84% ± 0.156% (accuracy), 99.84% ± 0.153% (precision), 99.84% ± 0.156% (sensitivity), and 99.84% ± 0.156% (F1-score). The result reaches the highest performance of the dataset for the lung cancer classification task. The proposed framework resolves the overfitting problem for lung cancer classification tasks, and it achieves better performance than other state-of-the-art methods.
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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 Socratic graph.