ArticlePeerJ. Computer science2021
Deepening into the suitability of using pre-trained models of ImageNet against a lightweight convolutional neural network in medical imaging: an experimental study.
Article in PeerJ. Computer science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed.
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- Convolutional automatic identification of B-lines and interstitial syndrome in lung ultrasound images using pre-trained neural networks with feature fusion.Frontiers in digital health · 2025Article
- Supervised representation learning based on various levels of pediatric radiographic views for transfer learning.Scientific reports · 2024Article
- Assessing heart rate and blood pressure estimation from image photoplethysmography using a digital blood pressure meter.Heliyon · 2024Article
- AI-organoid integrated systems for biomedical studies and applications.Bioengineering & translational medicine · 2024Review
- Trustworthy deep learning framework for the detection of abnormalities in X-ray shoulder images.PloS one · 2024Article
- Deep learning-mediated prediction of concealed accessory pathway based on sinus rhythmic electrocardiograms.Annals of noninvasive electrocardiology : the official journal of the International Society for Holter and Noninvasive Electrocardiology, Inc · 2023Article
- Deep Transfer Learning with Enhanced Feature Fusion for Detection of Abnormalities in X-ray Images.Cancers · 2023Article
- Dual Deep CNN for Tumor Brain Classification.Diagnostics (Basel, Switzerland) · 2023Article
- Classifying Malignancy in Prostate Glandular Structures from Biopsy Scans with Deep Learning.Cancers · 2023Article
- Robust Classification and Detection of Big Medical Data Using Advanced ParallelLife (Basel, Switzerland) · 2023Article
- Detection of abnormal extraocular muscles in small datasets of computed tomography images using a three-dimensional variational autoencoder.Scientific reports · 2023Article
- Deep learning-driven MRI trigeminal nerve segmentation with SEVB-net.Frontiers in neuroscience · 2023Article
- Brain Tumor Characterization Using Radiogenomics in Artificial Intelligence Framework.Cancers · 2022Review
- A Two-Stage Model for Predicting Mild Cognitive Impairment to Alzheimer's Disease Conversion.Frontiers in aging neuroscience · 2022Article
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9 authors.
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Abstract
Transfer learning (TL) has been widely utilized to address the lack of training data for deep learning models. Specifically, one of the most popular uses of TL has been for the pre-trained models of the ImageNet dataset. Nevertheless, although these pre-trained models have shown an effective performance in several domains of application, those models may not offer significant benefits in all instances when dealing with medical imaging scenarios. Such models were designed to classify a thousand classes of natural images. There are fundamental differences between these models and those dealing with medical imaging tasks regarding learned features. Most medical imaging applications range from two to ten different classes, where we suspect that it would not be necessary to employ deeper learning models. This paper investigates such a hypothesis and develops an experimental study to examine the corresponding conclusions about this issue. The lightweight convolutional neural network (CNN) model and the pre-trained models have been evaluated using three different medical imaging datasets. We have trained the lightweight CNN model and the pre-trained models with two scenarios which are with a small number of images once and a large number of images once again. Surprisingly, it has been found that the lightweight model trained from scratch achieved a more competitive performance when compared to the pre-trained model. More importantly, the lightweight CNN model can be successfully trained and tested using basic computational tools and provide high-quality results, specifically when using medical imaging datasets.
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