ArticleJournal of healthcare engineering2021
Intelligent Solutions in Chest Abnormality Detection Based on YOLOv5 and ResNet50.
Article in Journal of healthcare engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 9 papers.
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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.
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Who cites it
9 citing papers in PubMed, 47 citations in OpenAlex.
- Article
- Improved detection of aortic dissection in non-contrast-enhanced chest CT using an attention-based deep learning model.Heliyon · 2024Article
- Residual networks models detection of atrial septal defect from chest radiographs.La Radiologia medica · 2024Article
- Improving diagnosis accuracy with an intelligent image retrieval system for lung pathologies detection: a features extractor approach.Scientific reports · 2023Article
- Requirement of image standardization for AI-based macroscopic diagnosis for surgical specimens of gastric cancer.Journal of cancer research and clinical oncology · 2023Article
- Non-uniform image reconstruction for fast photoacoustic microscopy of histology imaging.Biomedical optics express · 2023Article
- Application of Artificial Intelligence in Anatomical Structure Recognition of Standard Section of Fetal Heart.Computational and mathematical methods in medicine · 2023Article
- Retracted: Intelligent Solutions in Chest Abnormality Detection Based on YOLOv5 and ResNet50.Journal of healthcare engineering · 2023Article
- Machine Learning Decision Support for Detecting Lipohypertrophy With Bedside Ultrasound: Proof-of-Concept Study.JMIR formative research · 2022Article
Corrections and comments
- Retraction · 2023-11-01Concerns/Issues about Data · Concerns/Issues about Results and/or Conclusions · Concerns/Issues about Referencing/Attributions · Concerns/Issues about Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Paper Mill · Computer-Aided Content or Computer-Generated Content · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
5 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computer-aided diagnosis (CAD) has nearly fifty years of history and has assisted many clinicians in the diagnosis. With the development of technology, recently, researches use the deep learning method to get high accuracy results in the CAD system. With CAD, the computer output can be used as a second choice for radiologists and contribute to doctors doing the final right decisions. Chest abnormality detection is a classic detection and classification problem; researchers need to classify common thoracic lung diseases and localize critical findings. For the detection problem, there are two deep learning methods: one-stage method and two-stage method. In our paper, we introduce and analyze some representative model, such as RCNN, SSD, and YOLO series. In order to better solve the problem of chest abnormality detection, we proposed a new model based on YOLOv5 and ResNet50. YOLOv5 is the latest YOLO series, which is more flexible than the one-stage detection algorithms before. The function of YOLOv5 in our paper is to localize the abnormality region. On the other hand, we use ResNet, avoiding gradient explosion problems in deep learning for classification. And we filter the result we got from YOLOv5 and ResNet. If ResNet recognizes that the image is not abnormal, the YOLOv5 detection result is discarded. The dataset is collected via VinBigData's web-based platform, VinLab. We train our model on the dataset using Pytorch frame and use the mAP, precision, and
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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.