Sürdürülebilir Kalkınma Amaçları
Yayınlar
- MakaleAçık Erişim2023Detection of pneumonia from pediatric chest X-ray images by transfer learning
Demir, Yasin; Bingöl, Özkan
When pathogens such as viruses, bacteria and fungi attack the lungs, the alveoli fill with inflamed fluid, causing pneumonia. Early diagnosis of this disease, which has fatal outcomes especially in children under 5 years old, is very important in controlling undesirable situations. Chest X-ray images play an important role in the diagnosis of pneumonia. In addition, the fact that the amount of radiation is lower than imaging devices such as tomography and the possibility of being accessible even from rural areas creates an advantage for X-ray devices. However, X-ray images that are not always clear or human conditions such as fatigue and lack of attention can make it difficult for specialists to detect pneumonia. In this study, a transfer learning-based convolutional neural network (CNN) approach is proposed, which can help specialists in the early and accurate diagnosis of pneumonia in children and, classify healthy and diseased individuals through Chest X-ray images. As a result of the study, an original CNN was proposed by adding additional layers to the AlexNet architecture layers and a test accuracy of 96.31% was obtained. Copyright 2021, Yıldız Technical University.
- BildiriAçık Erişim2021Pneumonia Detection from Pediatric Lung X-Ray Images with Convolutional Neural Network Method
Demir, Yasin; Bingöl, Ozkan
Pneumonia can be cause diseases such as cough, fever and respiratory problems and to death in the progressive situations. Mostly lung x-ray images are used for diagnosis of pneumonia. In this study, an unique convolutional neural network model that can help experts in detection of pneumonia using lung x-ray images below 5 years of age. In the model, open access to 5840 images has been used, and various data increasing methods were used to increase the performance of the training and the success of the method. With the proposed convolutional neural network model, an accuracy rate of 94.55% has been achieved.