Disease Recognition in X-ray Images with Doctor Consultation-Inspired Model

Phung, Kim Anh; Nguyen, Thuan Trong; Wangad, Nileshkumar; Baraheem, Samah Saeed; Vo, Nguyen D.; Nguyen, Khang

doi:10.3390/jimaging8120323

Cited by 5 publications

(3 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ChestXray [12] contains chest X-ray images of patients with COVID-19, pneumonia, and normal lungs. ChestXray dataset includes 6,432 X-ray images divided into two subsets.…”

Section: Experiments and Evaluation A Experimental Settings 1) Datasetsmentioning

confidence: 99%

“…• We investigate the performance of state-of-the-art transformer-based visual classification models on the ChestXray [12] and Clean-CC-CCII [13] datasets, which comprise Chest X-ray and CT scan images, respectively. • Inspired by the collaborative doctor consultation, We propose a novel hybrid model incorporating an early fusion strategy for combining transformer and CNN features, improving accuracy and efficiency in medical image analysis.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Collaborative Consultation Doctors Model: Unifying CNN and ViT for COVID-19 Diagnostic

Nguyen,

Tran

2023

IEEE Access

Self Cite

View full text Add to dashboard Cite

The COVID-19 pandemic presents significant challenges due to its high transmissibility and mortality risk. Traditional diagnostic methods, such as RT-PCR, have limitations that hinder timely and accurate screening. In response, AI-powered computer-aided imaging analysis techniques have emerged as a promising alternative for COVID-19 diagnosis. In this paper, we propose a novel approach that combines the strengths of Convolutional Neural Network (CNN) and Vision Transformer (ViT) to enhance the performance of COVID-19 diagnosis models. CNN excels at capturing spatial features in medical images, while ViT leverages self-attention mechanisms inspired by human radiologists. Additionally, our approach draws inspiration from subclinical diagnosis, a collaborative process involving attending physicians and specialists, which has proven effective in achieving accurate and comprehensive diagnoses. To this end, we employ an early fusion strategy integrating CNN and ViT, then fed into a residual neural network. By fusing these complementary features, our approach achieves state-of-the-art performance in accurately identifying COVID-19 cases on two benchmark datasets: Chest X-ray and Clean-CC-CCII. This research has the potential to enable timely and accurate screening, aiding in the early detection and management of COVID-19 cases. Our findings contribute to the growing knowledge of AI-powered diagnostic techniques and demonstrate the potential for advanced imaging analysis methods to support medical professionals in combating the ongoing pandemic.

show abstract

“…ChestXray [12] contains chest X-ray images of patients with COVID-19, pneumonia, and normal lungs. ChestXray dataset includes 6,432 X-ray images divided into two subsets.…”

Section: Experiments and Evaluation A Experimental Settings 1) Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Collaborative Consultation Doctors Model: Unifying CNN and ViT for COVID-19 Diagnostic

Nguyen,

Tran

2023

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

“…Due to the diminutive size of certain floating objects, detection errors are prone to arise during manual monitoring processes. With the application of computer vision, many fields have rapidly developed, including face recognition [12], autonomous driving [13], and medical image processing [14]. For the detection of floating objects in a river, we can use target detection based on the CNN (Convolutional Neural Network, CNN).…”

Section: Introductionmentioning

confidence: 99%

Detection of Floating Objects on Water Surface Using YOLOv5s in an Edge Computing Environment

Li,

Yang,

Zhang

et al. 2023

Water

View full text Add to dashboard Cite

Aiming to solve the problems with easy false detection of small targets in river floating object detection and deploying an overly large model, a new method is proposed based on improved YOLOv5s. A new data augmentation method for small objects is designed to enrich the dataset and improve the model’s robustness. Distinct feature extraction network levels incorporate different coordinate attention mechanism pooling methods to enhance the effective feature information extraction of small targets and improve small target detection accuracy. Then, a shallow feature map with 4-fold down-sampling is added, and feature fusion is performed using the Feature Pyramid Network. At the same time, bilinear interpolation replaces the up-sampling method to retain feature information and enhance the network’s ability to sense small targets. Network complex algorithms are optimized to better adapt to embedded platforms. Finally, the model is channel pruned to solve the problem of difficult deployment. The experimental results show that this method has a better feature extraction capability as well as a higher detection accuracy. Compared with the original YOLOv5 algorithm, the accuracy is improved by 15.7%, the error detection rate is reduced by 83% in small target task detection, the detection accuracy can reach 92.01% in edge testing, and the inference speed can reach 33 frames per second, which can meet the real-time requirements.

show abstract

Explainable image recognition models for aiding radiologists in clinical decision making

Michael,

Vasanth,

Sagayaradjy

et al. 2025

Data Science in the Medical Field

View full text Add to dashboard Cite

Disease Recognition in X-ray Images with Doctor Consultation-Inspired Model

Cited by 5 publications

References 33 publications

Collaborative Consultation Doctors Model: Unifying CNN and ViT for COVID-19 Diagnostic

Collaborative Consultation Doctors Model: Unifying CNN and ViT for COVID-19 Diagnostic

Detection of Floating Objects on Water Surface Using YOLOv5s in an Edge Computing Environment

Explainable image recognition models for aiding radiologists in clinical decision making

Contact Info

Product

Resources

About