ArticleCancers2022
An Effective Method for Lung Cancer Diagnosis from CT Scan Using Deep Learning-Based Support Vector Network.
Article in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Virtual histopathology methods in medical imaging - a systematic review.BMC medical imaging · 2024Pooled it
- HRKHu-Loss: harmonic regularization Kullback Huber loss-based lung cancer detection and severity level classification using CT image.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026Article
- Detection of lung cancer using Kronecker mobile forward harmonic net.Scientific reports · 2026Article
- LungDxFormer: a transformer-CNN hybrid model with dynamic spatial attention for accurate lung cancer detection and classification.Scientific reports · 2026Article
- WISeRKNet: wide slice residual Kronecker network for lung cancer detection based on CT images.Scientific reports · 2026Article
- Classification of pulmonary diseases using machine learning and deep learning models on GLI-2012 standardized spirometry features.BMC medical informatics and decision making · 2026Article
- Efficacy of pulmonary rehabilitation combined with minimal-dose adjunctive TCM (Shen Yi Capsules and Hua Chan Su) in improving postoperative outcomes for non-small cell lung cancer: a randomized controlled trial.Frontiers in medicine · 2026Article
- Article
- An Adaptive Hybrid Metaheuristic Algorithm for Lung Cancer in Pathological Image Segmentation.Diagnostics (Basel, Switzerland) · 2025Article
- A multi-technique ensemble model leveraging attention mechanism and image processing for enhanced colorectal tumor detection.Scientific reports · 2025Article
- Explainable AI for lung cancer detection via a custom CNN on CT images.Scientific reports · 2025Article
- Predicting COPD Readmission: An Intelligent Clinical Decision Support System.Diagnostics (Basel, Switzerland) · 2025Article
- Ensemble-based multiclass lung cancer classification using hybrid CNN-SVD feature extraction and selection method.PloS one · 2025Article
- Divulging Patterns: An Analytical Review for Machine Learning Methodologies for Breast Cancer Detection.Journal of Cancer · 2025Review
- Toward Robust Lung Cancer Diagnosis: Integrating Multiple CT Datasets, Curriculum Learning, and Explainable AI.Diagnostics (Basel, Switzerland) · 2024Article
- Lung and Colon Cancer Detection Using a Deep AI Model.Cancers · 2024Article
- Enhancing Lung Nodule Classification: A Novel CViEBi-CBGWO Approach with Integrated Image Preprocessing.Journal of imaging informatics in medicine · 2024Article
- Computer-aided diagnosis for lung cancer using waterwheel plant algorithm with deep learning.Scientific reports · 2024Article
- Are deep learning classification results obtained on CT scans fair and interpretable?Physical and engineering sciences in medicine · 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
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
The diagnosis of early-stage lung cancer is challenging due to its asymptomatic nature, especially given the repeated radiation exposure and high cost of computed tomography(CT). Examining the lung CT images to detect pulmonary nodules, especially the cell lung cancer lesions, is also tedious and prone to errors even by a specialist. This study proposes a cancer diagnostic model based on a deep learning-enabled support vector machine (SVM). The proposed computer-aided design (CAD) model identifies the physiological and pathological changes in the soft tissues of the cross-section in lung cancer lesions. The model is first trained to recognize lung cancer by measuring and comparing the selected profile values in CT images obtained from patients and control patients at their diagnosis. Then, the model is tested and validated using the CT scans of both patients and control patients that are not shown in the training phase. The study investigates 888 annotated CT scans from the publicly available LIDC/IDRI database. The proposed deep learning-assisted SVM-based model yields 94% accuracy for pulmonary nodule detection representing early-stage lung cancer. It is found superior to other existing methods including complex deep learning, simple machine learning, and the hybrid techniques used on lung CT images for nodule detection. Experimental results demonstrate that the proposed approach can greatly assist radiologists in detecting early lung cancer and facilitating the timely management of patients.
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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.