ReviewJournal of imaging2020
Deep Learning in Selected Cancers' Image Analysis-A Survey.
Review in Journal of imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 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
22 citing papers in PubMed, 66 citations in OpenAlex.
- A Hybrid Lightweight Deep Learning Framework for Colorectal Cancer Detection Using an Adaptive Gated Feature Fusion and Grid Search-Based Hyperparameter Optimization.Journal of imaging informatics in medicine · 2026Article
- Empathic AI for Patient-Centered Cancer Care: A Scoping Review of Patient Navigation, Support, and Clinical Practice.JMIR cancer · 2026Article
- Dual view deep learning for enhanced breast cancer screening using mammography.Scientific reports · 2024Article
- A Systematic Review of Real-Time Deep Learning Methods for Image-Based Cancer Diagnostics.Journal of multidisciplinary healthcare · 2024Article
- Skin Lesion Classification and Detection Using Machine Learning Techniques: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Meningioma brain tumor detection and classification using hybrid CNN method and RIDGELET transform.Scientific reports · 2023Article
- Comparative Analysis of Image Processing Techniques for Enhanced MRI Image Quality: 3D Reconstruction and Segmentation Using 3D U-Net Architecture.Diagnostics (Basel, Switzerland) · 2023Article
- Automated cornea diagnosis using deep convolutional neural networks based on cornea topography maps.Scientific reports · 2023Article
- WBM-DLNets: Wrapper-Based Metaheuristic Deep Learning Networks Feature Optimization for Enhancing Brain Tumor Detection.Bioengineering (Basel, Switzerland) · 2023Article
- A multidomain fusion model of radiomics and deep learning to discriminate between PDAC and AIP based onJapanese journal of radiology · 2023Article
- Gray level co-occurrence matrix and wavelet analyses reveal discrete changes in proximal tubule cell nuclei after mild acute kidney injury.Scientific reports · 2023Article
- Robustness Fine-Tuning Deep Learning Model for Cancers Diagnosis Based on Histopathology Image Analysis.Diagnostics (Basel, Switzerland) · 2023Article
- Clinical evaluation of deep learning-based clinical target volume three-channel auto-segmentation algorithm for adaptive radiotherapy in cervical cancer.BMC medical imaging · 2022Article
- A Comprehensive Survey on the Progress, Process, and Challenges of Lung Cancer Detection and Classification.Journal of healthcare engineering · 2022Review
- Lightweight Multireceptive Field CNN for 12-Lead ECG Signal Classification.Computational intelligence and neuroscience · 2022Article
- Diagnosis of Diabetes Mellitus Using Gradient Boosting Machine (LightGBM).Diagnostics (Basel, Switzerland) · 2021Article
- A Survey of Brain Tumor Segmentation and Classification Algorithms.Journal of imaging · 2021Article
- Automatic Detection of Colorectal Polyps Using Transfer Learning.Sensors (Basel, Switzerland) · 2021Article
- Multi-Input Dual-Stream Capsule Network for Improved Lung and Colon Cancer Classification.Diagnostics (Basel, Switzerland) · 2021Article
- Deep Learning in Medical Image Analysis.Journal of imaging · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning algorithms have become the first choice as an approach to medical image analysis, face recognition, and emotion recognition. In this survey, several deep-learning-based approaches applied to breast cancer, cervical cancer, brain tumor, colon and lung cancers are studied and reviewed. Deep learning has been applied in almost all of the imaging modalities used for cervical and breast cancers and MRIs for the brain tumor. The result of the review process indicated that deep learning methods have achieved state-of-the-art in tumor detection, segmentation, feature extraction and classification. As presented in this paper, the deep learning approaches were used in three different modes that include training from scratch, transfer learning through freezing some layers of the deep learning network and modifying the architecture to reduce the number of parameters existing in the network. Moreover, the application of deep learning to imaging devices for the detection of various cancer cases has been studied by researchers affiliated to academic and medical institutes in economically developed countries; while, the study has not had much attention in Africa despite the dramatic soar of cancer risks in the continent.
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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.