A deep learning approach for detecting pneumonia in chest X-rays

Şahin, Muhammet; Ulutaş, Hasan; Yüce, Esra

doi:10.31590/ejosat.1009434

Cited by 6 publications

(3 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The term "deep learning" refers to a category of machine learning algorithms that are used to program computers to think and act in ways that are analogous to those of humans. In other words, it is a method of learning and decision-making that attempts to emulate the human brain's functioning [15], [16].…”

Section: Deep Learningmentioning

confidence: 99%

A Deep Learning-Based Technique for Diagnosing Retinal Disease by Using Optical Coherence Tomography (OCT) Images

Şahin

2022

Turkish Journal of Science and Technology

Self Cite

View full text Add to dashboard Cite

The retina is a network layer that covers the back wall of the human eye and is made up of visual cells. The retina layer is the most complex and sensitive part of the eye, and disorders that affect it have a big impact on people's lives. The Optical Coherence Tomography (OCT) imaging technology can be used to diagnose diseases that arise because of pathological alterations in the retina. The importance of early diagnosis in the management of these illnesses cannot be overstated. In this article, a approach based on convolutional neural networks (CNN), a deep learning method, is presented for the detecting of retinal disorders from OCT images, based on the literature review. For disease detection and categorization, a CNN architecture is developed. The proposed method has been found to have a 94 percent accuracy rate in the identification of retinal disorders. In a deep learning application for classification, the proposed CNN network model is compared to the MobileNet50 network model available in the literature, and the results are provided. The evaluation parameter values for models trained using the 5-fold cross-validation approach for each type of disease in the retinal OCT images dataset are also included in the publication. The proposed method can clearly be utilized as a decision-making tool to assist clinicians in diagnosing retinal illnesses in a clinical context, based on its effectiveness thus far.

show abstract

Section: Deep Learningmentioning

confidence: 99%

A Deep Learning-Based Technique for Diagnosing Retinal Disease by Using Optical Coherence Tomography (OCT) Images

Şahin

2022

Turkish Journal of Science and Technology

Self Cite

View full text Add to dashboard Cite

show abstract

“…In recent years, many studies in the field of biomedicine have focused on computer‐aided diagnostic systems to facilitate the detection of various diseases. Advances in artificial intelligence also show that deep learning architectures have the capacity to diagnose at the level of healthcare professionals 1–6 . Machine learning methods have been widely used in the field of medicine in recent years and many studies have been presented in the literature 7–9 …”

Section: Introductionmentioning

confidence: 99%

Image processing and machine learning‐based bone fracture detection and classification using X‐ray images

Şahin

2023

Int J Imaging Syst Tech

View full text Add to dashboard Cite

One of the most important problems in orthopedics is undiagnosed or misdiagnosed bone fractures. This can lead to patients receiving an incorrect diagnosis or treatment, which can result in a longer treatment period. In this study, fracture detection and classification are performed using various machine learning techniques using of a dataset containing various bones (normal and fractured). Firstly, the X‐ray images obtained are subjected to image preprocessing stages and are prepared for the feature extraction stage. Then, in addition to the Canny and Sobel edge detection methods used in the image processing stage, feature extraction of X‐ray images is performed with the help of Houhg line detection and Harris corner detector. The data obtained by performing feature extraction is given to 12 different machine learning classifiers and the results are presented. Setting hyperparameters for classifiers is done by the grid search method, and the study is tested for 10‐fold cross‐validation. Classifier results are presented comparatively as accuracy, training time, and testing time, and linear discriminant analysis (LDA) reaches the highest accuracy rate with 88.67% and 0.89 AUC. The proposed computer‐aided diagnosis system (CAD) will reduce the burden on physicians by identifying fractures with high accuracy.

show abstract

“…In the past five years, a large number of solutions were developed intended to computer analysis of radiological studies, and, now, numerous approaches are known for classification, detection, and segmentation of target artefacts in radiological images [ 2 , 5 – 7 , 14 ]. In this study we merge the best practices of solving the classification and detection problems in computer vision for constructing a time-efficient and highly accurate program solution to analyze the radiological studies of the thoracic cage.…”

Section: Introductionmentioning

confidence: 99%

Research and Development of Deep Learning Algorithms for the Classification of Pneumonia Type and Detection of Ground-Glass Loci on Radiological Images

Emchinov

Ryazanov

2022

Pattern Recognit. Image Anal.

View full text Add to dashboard Cite

Pneumonia is a highly dangerous state that poses serious risks to the health of a patient. In contrast to common pneumonia, lung disease COVID-19 causes a large number of lethal outcomes. The pneumonia of a viral etiology caused by the RNA virus SARS-CoV-2 is visually hardly distinguishable from the bacterial pneumonia or inflammation caused by other viral infections. Now, COVID-19 can be diagnosed using PCR tests or X-rays of the thoracic cage. However, the results of a molecular study take a long time to prepare. In contrast, the radiological images of the thoracic cage can be obtained immediately after the radiological study. Although there exist guiding principles which help radiologists to differentiate COVID-19 from other types of infections, their assessments differ. In addition, doctors who are not radiologists can be assisted in better locating the disease, for instance, by a bounding box. Development of precise computer methods based on artificial intelligence can help medical workers in quickly determining the type of pneumonia and detecting the loci of inflammation. In this study a package of methods is developed to determine the type of pneumonia and detect the ground-glass loci using the appropriate architectures of neural networks, loss functions, augmentations at the training data generation stage, test time augmentation, and computer vision model ensembles. This task is successfully solved in the SIIM-FISABIO-RSNA COVID-19 Detection competition [17] and the proposed algorithm is in the top 10% of the best solutions.

show abstract

A deep learning approach for detecting pneumonia in chest X-rays

Cited by 6 publications

References 19 publications

A Deep Learning-Based Technique for Diagnosing Retinal Disease by Using Optical Coherence Tomography (OCT) Images

A Deep Learning-Based Technique for Diagnosing Retinal Disease by Using Optical Coherence Tomography (OCT) Images

Image processing and machine learning‐based bone fracture detection and classification using X‐ray images

Research and Development of Deep Learning Algorithms for the Classification of Pneumonia Type and Detection of Ground-Glass Loci on Radiological Images

Contact Info

Product

Resources

About