ReviewCancers2022
Deep Learning Techniques to Diagnose Lung Cancer.
Review in Cancers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
36 citing papers in PubMed, 190 citations in OpenAlex.
- An intelligent lung cancer detection from computed tomography images using robust optimal deep transfer learning with XAI model.BMC medical informatics and decision making · 2026Article
- LungDxFormer: a transformer-CNN hybrid model with dynamic spatial attention for accurate lung cancer detection and classification.Scientific reports · 2026Article
- Review
- Interpretable hybrid ensemble with attention-based fusion and EAOO-GA optimization for lung cancer detection.Scientific reports · 2026Article
- A deep learning model to enhance lung cancer detection using 'Dual-Branch' model classification approach.PloS one · 2026Article
- Deep Learning Model-Based Architectures for Lung Tumor Mutation Profiling: A Systematic Review.Cancers · 2025Review
- Early Lung Cancer Detection via AI-Enhanced CT Image Processing Software.Diagnostics (Basel, Switzerland) · 2025Article
- Lung Nodule Malignancy Classification Integrating Deep and Radiomic Features in a Three-Way Attention-Based Fusion Module.Journal of imaging · 2025Article
- ViT-DCNN: Vision Transformer with Deformable CNN Model for Lung and Colon Cancer Detection.Cancers · 2025Article
- Deep Learning Approaches for Automated Prediction of Treatment Response in Non-Small-Cell Lung Cancer Patients Based on CT and PET Imaging.Tomography (Ann Arbor, Mich.) · 2025Review
- Smart nanomedicines powered by artificial intelligence: a breakthrough in lung cancer diagnosis and treatment.Medical oncology (Northwood, London, England) · 2025Review
- Artificial intelligence based classification and prediction of medical imaging using a novel framework of inverted and self-attention deep neural network architecture.Scientific reports · 2025Article
- Integrating artificial intelligence with smartphone-based imaging for cancer detection in vivo.Biosensors & bioelectronics · 2025Review
- Classification of NSCLC subtypes using lung microbiome from resected tissue based on machine learning methods.NPJ systems biology and applications · 2025Article
- Deep learning models for CT image classification: a comprehensive literature review.Quantitative imaging in medicine and surgery · 2025Review
- SVMVGGNet-16: A Novel Machine and Deep Learning Based Approaches for Lung Cancer Detection Using Combined SVM and VGGNet-16.Current medical imaging · 2025Article
- Machine learning for automated classification of lung collagen in a urethane-induced lung injury mouse model.Biomedical optics express · 2024Article
- A Method for Real-Time Lung Nodule Instance Segmentation Using Deep Learning.Life (Basel, Switzerland) · 2024Article
- Are deep learning classification results obtained on CT scans fair and interpretable?Physical and engineering sciences in medicine · 2024Article
- Bibliometric analysis of the application of deep learning in cancer from 2015 to 2023.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
Abstract
Medical imaging tools are essential in early-stage lung cancer diagnostics and the monitoring of lung cancer during treatment. Various medical imaging modalities, such as chest X-ray, magnetic resonance imaging, positron emission tomography, computed tomography, and molecular imaging techniques, have been extensively studied for lung cancer detection. These techniques have some limitations, including not classifying cancer images automatically, which is unsuitable for patients with other pathologies. It is urgently necessary to develop a sensitive and accurate approach to the early diagnosis of lung cancer. Deep learning is one of the fastest-growing topics in medical imaging, with rapidly emerging applications spanning medical image-based and textural data modalities. With the help of deep learning-based medical imaging tools, clinicians can detect and classify lung nodules more accurately and quickly. This paper presents the recent development of deep learning-based imaging techniques for early lung cancer detection.
Indexed as
Identifiers
What OpenQuestion holds
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.