Chest X-Ray Investigation: A Convolutional Neural Network Approach

Das, Tapan Kumar; Chowdhary, Chiranji Lal; Gao, Xiao‐Zhi

doi:10.4028/www.scientific.net/jbbbe.45.57

Cited by 23 publications

(10 citation statements)

References 33 publications

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Section: Introductionmentioning

confidence: 99%

“…Since early months of 2020, coronavirus disease (COVID- 19), which is considerably contagious has permeated through the globe [1,2]. It has imposed significant and unprecedented sufferings and threats for premature death [2]. Unequivocally, it is now regarded as the most deadly and dangerous disease that makes severe panic to the crowd [3].…”

Section: Introductionmentioning

confidence: 99%

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COVID-19 Diagnosis: ULGFBP-ResNet51 approach on the CT and the Chest X-ray Images Classification

Esmaeili,

Mohassel Feghhi,

Shahdi

2024

Scientia Iranica

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The contagious and pandemic COVID-19 disease is currently considered as the main health concern and posed widespread panic across human-beings. It affects the human respiratory tract and lungs intensely. So that it has imposed significant threats for premature death. Although, its early diagnosis can play a vital role in revival phase, the radiography tests with the manual intervention are a time-consuming process. Time is also limited for such manual inspecting of numerous patients in the hospitals. Thus, the necessity of automatic diagnosis on the chest X-ray or the CT images with a high efficient performance is urgent. Toward this end, we propose a novel method, named as the ULGFBP-ResNet51 to tackle with the COVID-19 diagnosis in the images. In fact, this method includes Uniform Local Binary Pattern (ULBP), Gabor Filter (GF), and ResNet51. According to our results, this method could offer superior performance in comparison with the other methods, and attain maximum accuracy.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

COVID-19 Diagnosis: ULGFBP-ResNet51 approach on the CT and the Chest X-ray Images Classification

Esmaeili,

Mohassel Feghhi,

Shahdi

2024

Scientia Iranica

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“…At the 100th training iteration, the mean square error and the false recognition rate dropped below 1.1%, suggesting that the LPRNN was trained correctly. Edge preservation index values were above the experimental threshold of 0.48, signal-to-noise ratios (SNRs) were greater than 65 dB, peak SNR ratios were greater than 70 dB, and destruction times were faster [ 28 ]. Principal Component Analysis improved characteristics, while the Co-Active Adaptive Neuro-Fuzzy Expert System sorted images of the brain into glioma or non-glioma groups.…”

Section: Introductionmentioning

confidence: 99%

A Framework of Faster CRNN and VGG16-Enhanced Region Proposal Network for Detection and Grade Classification of Knee RA

et al. 2023

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We developed a framework to detect and grade knee RA using digital X-radiation images and used it to demonstrate the ability of deep learning approaches to detect knee RA using a consensus-based decision (CBD) grading system. The study aimed to evaluate the efficiency with which a deep learning approach based on artificial intelligence (AI) can find and determine the severity of knee RA in digital X-radiation images. The study comprised people over 50 years with RA symptoms, such as knee joint pain, stiffness, crepitus, and functional impairments. The digitized X-radiation images of the people were obtained from the BioGPS database repository. We used 3172 digital X-radiation images of the knee joint from an anterior–posterior perspective. The trained Faster-CRNN architecture was used to identify the knee joint space narrowing (JSN) area in digital X-radiation images and extract the features using ResNet-101 with domain adaptation. In addition, we employed another well-trained model (VGG16 with domain adaptation) for knee RA severity classification. Medical experts graded the X-radiation images of the knee joint using a consensus-based decision score. We trained the enhanced-region proposal network (ERPN) using this manually extracted knee area as the test dataset image. An X-radiation image was fed into the final model, and a consensus decision was used to grade the outcome. The presented model correctly identified the marginal knee JSN region with 98.97% of accuracy, with a total knee RA intensity classification accuracy of 99.10%, with a sensitivity of 97.3%, a specificity of 98.2%, a precision of 98.1%, and a dice score of 90.1% compared with other conventional models.

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“…Such a system could both be a valuable tool for medical education and help reduce variability in the diagnostic precision of skilled and unskilled examiners. Furthermore, AI-based computer-aided diagnosis tools can play an important role in assisting doctors, especially in remote areas with a lack of medical support [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Deep Recognition of Standardized Liver Ultrasound Scan Locations

Shin

Lee

2023

Sensors

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Liver ultrasound (US) plays a critical role in diagnosing liver diseases. However, it is often difficult for examiners to accurately identify the liver segments captured in US images due to patient variability and the complexity of US images. Our study aim is automatic, real-time recognition of standardized US scans coordinated with reference liver segments to guide examiners. We propose a novel deep hierarchical architecture for classifying liver US images into 11 standardized US scans, which has yet to be properly established due to excessive variability and complexity. We address this problem based on a hierarchical classification of 11 US scans with different features applied to individual hierarchies as well as a novel feature space proximity analysis for handling ambiguous US images. Experiments were performed using US image datasets obtained from a hospital setting. To evaluate the performance under patient variability, we separated the training and testing datasets into distinct patient groups. The experimental results show that the proposed method achieved an F1-score of more than 93%, which is more than sufficient for a tool to guide examiners. The superior performance of the proposed hierarchical architecture was demonstrated by comparing its performance with that of non-hierarchical architecture.

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Chest X-Ray Investigation: A Convolutional Neural Network Approach

Cited by 23 publications

References 33 publications

COVID-19 Diagnosis: ULGFBP-ResNet51 approach on the CT and the Chest X-ray Images Classification

COVID-19 Diagnosis: ULGFBP-ResNet51 approach on the CT and the Chest X-ray Images Classification

A Framework of Faster CRNN and VGG16-Enhanced Region Proposal Network for Detection and Grade Classification of Knee RA

Real-Time Deep Recognition of Standardized Liver Ultrasound Scan Locations

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