ArticleScientific reports2021
Hierarchical deep learning models using transfer learning for disease detection and classification based on small number of medical images.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning and optical coherence tomography in glaucoma: Bridging the diagnostic gap on structural imaging.Frontiers in ophthalmology · 2022Pooled it
- High-accuracy retinal age prediction via fundus-based multitask learning reveals the effect of systemic disease.Communications medicine · 2026Article
- Application of smooth OWA operators to classification of retinitis pigmentosa.Scientific reports · 2026Article
- Hierarchical Mechanistic Modeling of Complex Toxicity Endpoints from Public Concentration-Response Data.Environmental science & technology · 2026Article
- Distributed Deep Learning in IoT Sensor Network for the Diagnosis of Plant Diseases.Sensors (Basel, Switzerland) · 2025Article
- Predictive modelling in times of public health emergencies: patients' non-transport decisions during the COVID-19 pandemic.BMC emergency medicine · 2025Article
- Category-Aware Two-Stage Divide-and-Ensemble Framework for Sperm Morphology Classification.Diagnostics (Basel, Switzerland) · 2025Article
- Early detection of pancreatic cancer on computed tomography: advancements with deep learning.Radiology advances · 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
- Reinforcement-based leveraging transfer learning for multiclass optical coherence tomography images classification.Scientific reports · 2025Article
- Hierarchical image classification using transfer learning to improve deep learning model performance for amazon parrots.Scientific reports · 2025Article
- An annotated heterogeneous ultrasound database.Scientific data · 2025Article
- Article
- A hierarchical deep learning approach for diagnosing impacted canine-induced root resorption via cone-beam computed tomography.BMC oral health · 2024Article
- A practical guide to the implementation of artificial intelligence in orthopaedic research-Part 2: A technical introduction.Journal of experimental orthopaedics · 2024Review
- Severity of error in hierarchical datasets.Scientific reports · 2023Article
- Optical Coherence Tomography Image Classification Using Hybrid Deep Learning and Ant Colony Optimization.Sensors (Basel, Switzerland) · 2023Article
- MediNet: transfer learning approach with MediNet medical visual database.Multimedia tools and applications · 2023Article
- Transfer Learning Approach and Nucleus Segmentation with MedCLNet Colon Cancer Database.Journal of digital imaging · 2023Article
- A review of multi-omics data integration through deep learning approaches for disease diagnosis, prognosis, and treatment.Frontiers in genetics · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning is being employed in disease detection and classification based on medical images for clinical decision making. It typically requires large amounts of labelled data; however, the sample size of such medical image datasets is generally small. This study proposes a novel training framework for building deep learning models of disease detection and classification with small datasets. Our approach is based on a hierarchical classification method where the healthy/disease information from the first model is effectively utilized to build subsequent models for classifying the disease into its sub-types via a transfer learning method. To improve accuracy, multiple input datasets were used, and a stacking ensembled method was employed for final classification. To demonstrate the method's performance, a labelled dataset extracted from volumetric ophthalmic optical coherence tomography data for 156 healthy and 798 glaucoma eyes was used, in which glaucoma eyes were further labelled into four sub-types. The average weighted accuracy and Cohen's kappa for three randomized test datasets were 0.839 and 0.809, respectively. Our approach outperformed the flat classification method by 9.7% using smaller training datasets. The results suggest that the framework can perform accurate classification with a small number of medical images.
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.