Chest X-ray analysis empowered with deep learning: A systematic review

Meedeniya, Dulani; Kumarasinghe, Hashara; Kolonne, Shammi; Fernando, Chamodi; Díez, Isabel de la Torre; Marques, Gonçalo

doi:10.1016/j.asoc.2022.109319

Cited by 45 publications

(8 citation statements)

References 111 publications

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“…Ref. [ 19 ] presents a good review study about DL-based COVID-19 detection method using CXR images. Another [ 20 ] study examines existing deep learning methods for detecting coronavirus infection in lung images.…”

Section: Literature Reviewmentioning

confidence: 99%

Computer-Aided Diagnosis of COVID-19 from Chest X-ray Images Using Hybrid-Features and Random Forest Classifier

et al. 2023

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In recent years, a lot of attention has been paid to using radiology imaging to automatically find COVID-19. (1) Background: There are now a number of computer-aided diagnostic schemes that help radiologists and doctors perform diagnostic COVID-19 tests quickly, accurately, and consistently. (2) Methods: Using chest X-ray images, this study proposed a cutting-edge scheme for the automatic recognition of COVID-19 and pneumonia. First, a pre-processing method based on a Gaussian filter and logarithmic operator is applied to input chest X-ray (CXR) images to improve the poor-quality images by enhancing the contrast, reducing the noise, and smoothing the image. Second, robust features are extracted from each enhanced chest X-ray image using a Convolutional Neural Network (CNNs) transformer and an optimal collection of grey-level co-occurrence matrices (GLCM) that contain features such as contrast, correlation, entropy, and energy. Finally, based on extracted features from input images, a random forest machine learning classifier is used to classify images into three classes, such as COVID-19, pneumonia, or normal. The predicted output from the model is combined with Gradient-weighted Class Activation Mapping (Grad-CAM) visualisation for diagnosis. (3) Results: Our work is evaluated using public datasets with three different train–test splits (70–30%, 80–20%, and 90–10%) and achieved an average accuracy, F1 score, recall, and precision of 97%, 96%, 96%, and 96%, respectively. A comparative study shows that our proposed method outperforms existing and similar work. The proposed approach can be utilised to screen COVID-19-infected patients effectively. (4) Conclusions: A comparative study with the existing methods is also performed. For performance evaluation, metrics such as accuracy, sensitivity, and F1-measure are calculated. The performance of the proposed method is better than that of the existing methodologies, and it can thus be used for the effective diagnosis of the disease.

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Section: Literature Reviewmentioning

confidence: 99%

Computer-Aided Diagnosis of COVID-19 from Chest X-ray Images Using Hybrid-Features and Random Forest Classifier

et al. 2023

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“…The model is trained using a cross-entropy loss function, which compares each image’s predicted and actual probabilities [ 5 ]. To make the model better, researchers used data augmentation techniques to create a more diverse training dataset by resizing or rotating the original images [ 55 ]. During training, the model was fed a fixed number of images at a time (a batch), which was repeated multiple times [ 56 ].…”

Section: Significance Of You Only Look Once (Yolo) V8 Algorithmmentioning

confidence: 99%

Using Computer Vision Techniques to Automatically Detect Abnormalities in Chest X-rays

Mustafa,

Nsour

2023

Diagnostics

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Our research focused on creating an advanced machine-learning algorithm that accurately detects anomalies in chest X-ray images to provide healthcare professionals with a reliable tool for diagnosing various lung conditions. To achieve this, we analysed a vast collection of X-ray images and utilised sophisticated visual analysis techniques; such as deep learning (DL) algorithms, object recognition, and categorisation models. To create our model, we used a large training dataset of chest X-rays, which provided valuable information for visualising and categorising abnormalities. We also utilised various data augmentation methods; such as scaling, rotation, and imitation; to increase the diversity of images used for training. We adopted the widely used You Only Look Once (YOLO) v8 algorithm, an object recognition paradigm that has demonstrated positive outcomes in computer vision applications, and modified it to classify X-ray images into distinct categories; such as respiratory infections, tuberculosis (TB), and lung nodules. It was particularly effective in identifying unique and crucial outcomes that may, otherwise, be difficult to detect using traditional diagnostic methods. Our findings demonstrate that healthcare practitioners can reliably use machine learning (ML) algorithms to diagnose various lung disorders with greater accuracy and efficiency.

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“…Artificial Intelligence (AI) has greatly advanced healthcare in recent years, particularly in medical imaging technologies such as computed tomography (CT) scans, X-rays, and ultrasonography [12][13][14][15][16] . AI has also contributed to increased speed and efficiency in medical image analysis, reducing the workload of healthcare professionals and improving patient outcomes.…”

Section: Introductionmentioning

confidence: 99%

PACT-3D, a Deep Learning Algorithm for Pneumoperitoneum Detection in Abdominal CT Scans

Chiu,

Huang,

Kuo

2024

Preprint

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Pneumoperitoneum, necessitates surgical intervention in 85-90% of cases, relies heavily on CT scans for diagnosis. Delay or misdiagnosis in detecting pneumoperitoneum can significantly increase mortality and morbidity. Our study introduced PACT-3D, a deep learning model developed to identify pneumoperitoneum in CT images. In this single hospital study, we retrospectively reviewed abdominal CT scans from January 2012 to December 2021, excluded CT of image acquisition error and without reports to form the development dataset for training the model. We evaluated the PACT- 3D model using a simulated test set of 14,039 scans and a prospective test set of 6,351 scans, collected from December 2022 to May 2023 at the same center. PACT-3D achieved a sensitivity of 0.81 and a specificity of 0.99 in retrospective testing, with prospective validation yielding a sensitivity of 0.83 and a specificity of 0.99. Sensitivity improved to 0.95 and 0.98 when excluding cases with a small amount of free air (total volume < 10ml) in simulated and prospective test sets, respectively. By delivering accurate and consistent patient-level predictions and providing segmented masks, PACT- 3D holds significant potential for assisting rapid decision-making in emergency care, thereby potentially improving patient outcomes.

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Chest X-ray analysis empowered with deep learning: A systematic review

Cited by 45 publications

References 111 publications

Computer-Aided Diagnosis of COVID-19 from Chest X-ray Images Using Hybrid-Features and Random Forest Classifier

Computer-Aided Diagnosis of COVID-19 from Chest X-ray Images Using Hybrid-Features and Random Forest Classifier

Using Computer Vision Techniques to Automatically Detect Abnormalities in Chest X-rays

PACT-3D, a Deep Learning Algorithm for Pneumoperitoneum Detection in Abdominal CT Scans

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