ArticleComputational and mathematical methods in medicine2021
PSSPNN: PatchShuffle Stochastic Pooling Neural Network for an Explainable Diagnosis of COVID-19 with Multiple-Way Data Augmentation.
Article in Computational and mathematical methods in medicine, 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 It has been retracted, and should not be counted.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 25 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
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Role of Artificial Intelligence in COVID-19 Detection.Sensors (Basel, Switzerland) · 2021Pooled it
- Challenges issues and future recommendations of deep learning techniques for SARS-CoV-2 detection utilising X-ray and CT images: a comprehensive review.PeerJ. Computer science · 2024Article
- EAPR: explainable and augmented patient representation learning for disease prediction.Health information science and systems · 2023Article
- Heart failure patients monitoring using IoT-based remote monitoring system.Scientific reports · 2023Article
- Monitoring Acute Heart Failure Patients Using Internet-of-Things-Based Smart Monitoring System.Sensors (Basel, Switzerland) · 2023Article
- An Analysis of Image Features Extracted by CNNs to Design Classification Models for COVID-19 and Non-COVID-19.Journal of signal processing systems · 2023Article
- Retracted: PSSPNN: PatchShuffle Stochastic Pooling Neural Network for an Explainable Diagnosis of COVID-19 with Multiple-Way Data Augmentation.Computational and mathematical methods in medicine · 2023Article
- A Survey on Deep Learning in COVID-19 Diagnosis.Journal of imaging · 2022Review
- Explaining COVID-19 diagnosis with Taylor decompositions.Neural computing & applications · 2022Article
- An intelligent forecast for COVID-19 based on single and multiple features.International journal of intelligent systems · 2022Article
- LiteCovidNet: A lightweight deep neural network model for detection of COVID-19 using X-ray images.International journal of imaging systems and technology · 2022Article
- EDNC: Ensemble Deep Neural Network for COVID-19 Recognition.Tomography (Ann Arbor, Mich.) · 2022Article
- Pooling in convolutional neural networks for medical image analysis: a survey and an empirical study.Neural computing & applications · 2022Review
- VISPNN: VGG-inspired Stochastic Pooling Neural Network.Computers, materials & continua · 2022Article
- Breast Cancer Detection on Histopathological Images Using a Composite Dilated Backbone Network.Computational intelligence and neuroscience · 2022Article
- Secondary Pulmonary Tuberculosis Identification Via pseudo-Zernike Moment and Deep Stacked Sparse Autoencoder.Journal of grid computing · 2022Article
- MIDCAN: A multiple input deep convolutional attention network for Covid-19 diagnosis based on chest CT and chest X-ray.Pattern recognition letters · 2021Article
- Prediction of Mental Health in Medical Workers During COVID-19 Based on Machine Learning.Frontiers in public health · 2021Article
- ADVIAN: Alzheimer's Disease VGG-Inspired Attention Network Based on Convolutional Block Attention Module and Multiple Way Data Augmentation.Frontiers in aging neuroscience · 2021Article
- Integration and Segregation of Dynamic Functional Connectivity States for Mild Cognitive Impairment Revealed by Graph Theory Indicators.Contrast media & molecular imaging · 2021Article
Corrections and comments
- Retraction · 2023-09-27Concerns/Issues about Data · Concerns/Issues about Results and/or Conclusions · Concerns/Issues about Referencing/Attributions · Concerns/Issues about Peer Review · Informed/Patient Consent - None/Withdrawn · Investigation by Journal/Publisher · Investigation by Third Party · Lack of IRB/IACUC Approval and/or Compliance · Paper Mill · Computer-Aided Content or Computer-Generated Content · Unreliable Results and/or Conclusions · · See also: https://pubpeer.com/publications/CF1CE25FB861BA2F8FE9121FABD069
- Retracted
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimCOVID-19 has caused large death tolls all over the world. Accurate diagnosis is of significant importance for early treatment.
methodsIn this study, we proposed a novel PSSPNN model for classification between COVID-19, secondary pulmonary tuberculosis, community-captured pneumonia, and healthy subjects. PSSPNN entails five improvements: we first proposed the n-conv stochastic pooling module. Second, a novel stochastic pooling neural network was proposed. Third, PatchShuffle was introduced as a regularization term. Fourth, an improved multiple-way data augmentation was used. Fifth, Grad-CAM was utilized to interpret our AI model.
resultsThe 10 runs with random seed on the test set showed our algorithm achieved a microaveraged F1 score of 95.79%. Moreover, our method is better than nine state-of-the-art approaches.
conclusionThis proposed PSSPNN will help assist radiologists to make diagnosis more quickly and accurately on COVID-19 cases.
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.