Wyniki wyszukiwania - BazTech

1

Enhancing Multiclass Pneumonia Classification with Machine Learning and Textural Features

Godbin A. Beena, Jasmine S. Graceline

Machine Graphics and Vision

|

2023

|

Vol. 32, No. 3/4

83--106

EN

The highly infectious and mutating COVID-19, known as the novel coronavirus, poses a substantial threat to both human health and the global economy. Detecting COVID-19 early presents a challenge due to its resemblance to pneumonia. However, distinguishing between the two is critical for saving lives. Chest X-rays, empowered by machine learning classifiers and ensembles, prove effective in identifying multiclass pneumonia in the lungs, leveraging textural characteristics such as GLCM and GLRLM. These textural features are instilled into the classifiers and ensembles within the domain of machine learning. This article explores the multiclass categorization of X-ray images across four categories: COVID-19-impacted, bacterial pneumonia-affected, viral pneumonia-affected, and normal lungs. The classification employs Random Forest, Support Vector Machine, K-Nearest Neighbor, LGBM, and XGBoost. Random Forest and LGBM achieve an impressive accuracy of 92.4% in identifying GLCM features. The network’s performance is evaluated based on accuracy, precision, sensitivity and F1-score.

2

A novel deep learning-based approach for prediction of neonatal respiratory disorders from chest X-ray images

Erdogan Yildirim Ayse, Canayaz Murat

Biocybernetics and Biomedical Engineering

|

2023

|

Vol. 43, no. 4

635--655

EN

In recent years, many diseases can be diagnosed in a short time with the use of deep learning models in the field of medicine. Most of the studies in this area focus on adult or pediatric patients. However, deep learning studies for the diagnosis of diseases in neonatal are not sufficient. Also, since it is known that respiratory disorders such as pneumonia have a large place among the causes of neonatal death, early and accurate diagnosis of respiratory diseases in neonates is crucial. For this reason, our study aims to detect the presence of respiratory disorders through the developed deep-learning approach using chest X-ray images of patients hospitalized in the Neonatal Intensive Care Unit. Accordingly, the enhanced version of C+EffxNet, the new hybrid deep learning model, is designed to predict respiratory disorders in neonates. In this version, the features selected by PCA are combined as 100, 200, and 300, then the binary classification process was carried out. In the study, the accuracy and kappa value were obtained as 0.965, and 0.904, respectively before feature merging, while these values were obtained as 0.977, and 0.935 after feature merging. This method, which was developed for the diagnosis of respiratory disorders in neonates, was also subsequently applied to a chest X-ray dataset that is frequently used in the literature for the diagnosis of pediatric pneumonia. For this data set, while the accuracy was 0.992, the kappa value was 0.982. The results obtained confirm the success of the proposed method for both datasets.

3

Porównanie parametrów ekspozycji do zdjęcia klatki piersiowej dorosłych pacjentów przyjętych na oddział ratunkowy Szpitala Uniwersyteckiego w Krakowie z parametrami zawartymi w Procedurach Wzorcowych (Prawo atomowe – Dz.U. z 2014 r.)

Mizgalska-Traczyk Halina, Borkowska Eliza, Malisz Piotr

Inżynier i Fizyk Medyczny

|

2019

|

Vol. 8, nr 5

373--377

PL

Zdjęcie klatki piersiowej jest podstawowym zdjęciem w diagnostyce obrazowej, jednym z najczęściej wykonywanych, a niezbędnym u pacjentów przyjętych na szpitalny oddział ratunkowy. Wzorcowe procedury radiologiczne opublikowane w Dz. Urz. Ministra Zdrowia z 2014 poz. 85 zobowiązują personel medyczny do wykonywania przeglądowego badania rentgenowskiego klatki piersiowej zgodnie z przyjętymi standardami wykonawczymi, aby zminimalizować możliwe skutki niepożądane związane z promieniowaniem jonizującym. W ramach prowadzonego audytu wykazano, że 67% badań przeglądowych klatki piersiowej wykonano według standardu techniką twardego promieniowania w pozycji stojącej w rzucie PA, natomiast 33% wykonano miękką falą w pozycji AP na leżąco. W niniejszym opracowaniu autorzy proponują zmianę podejścia techników elektroradiologii do wyboru parametrów ekspozycji u pacjentów diagnozowanych w ciężkich stanach w szpitalnych oddziałach ratunkowych i zastosowanie do zdjęć klatki piersiowej u tych pacjentów techniki twardego promieniowania zgodnie z zaleceniami zawartymi we wzorcowych procedurach radiologicznych. Technikę obniżonego napięcia stosować przy wykonywaniu zdjęcia przyłóżkowego u pacjentów hospitalizowanych lub w sytuacjach, kiedy stosowanie twardego promieniowania z powodów technicznych w pracowni RTG jest niemożliwe.

EN

Chest X-ray is a basic diagnostic image, one of the most frequently performed on patients admitted to the hospital emergency department. The reference radiological procedures published by the Minister of Health in 2014 obliges medical personnel to perform a chest X-ray examination in accordance with the adopted executive standards in order to minimize possible adverse effects connected with ionizing radiation. An audit demonstrated that 67% of chest examinations were made according to the standard, using a hard radiation technique in the standing PA view, while 33% were performed in a soft radiation technique AP in a lying position. In this study the authors suggest to change the approach of X-ray technicians to the choice of exposure parameters in the case of patients with severe conditions in hospital emergency departments and use hard radiation techniques for chest images in the case of these patients in accordance with the recommendations in the reference radiological procedures. The technique of reduced voltage should be used during bedside examinations in the case of hospitalized patients or in situations where it is not possible to use hard radiation in an X-ray department for technical reasons.