Önceden Eği̇ti̇lmi̇ş Deri̇n Ağlar İle Göğüs Röntgeni̇ Görüntüleri̇ Kullanarak Pnömoni̇ Siniflandirilmasi

Er, Mehmet Bilal

doi:10.36306/konjes.794505

Konya Journal of Engineering Sciences

2021

DOI: 10.36306/konjes.794505

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Önceden Eği̇ti̇lmi̇ş Deri̇n Ağlar İle Göğüs Röntgeni̇ Görüntüleri̇ Kullanarak Pnömoni̇ Siniflandirilmasi

Mehmet Bilal Er¹

Abstract: Pnömoni, bakterilerin, virüslerin veya mantarların neden olabileceği bir akciğer enfeksiyonudur. Enfeksiyon, akciğerlerin hava keselerinin iltihaplanmasına ve sıvı veya irin ile dolmasına neden olur. Ciddi ve hayatı tehdit eden bir hastalık olabilir. Dünya genelinde her yıl pnömoni nedeniyle çok sayıda kişi ölmektedir. Pnömoninin erken tespiti ve tedavisi, ölüm oranlarını önemli ölçüde azaltabilmektedir. Bu nedenle, bu araştırmada pnömoniyi tespit etmek için röntgen görüntüleri kullanarak önceden eğitilmiş der… Show more

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Cited by 2 publications

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Yapay Sinir Ağları Kullanılarak Pediatrik Akciğer Röntgen Görüntülerinden Pnömoni Tespiti

DÜNDAR,

KOÇER

2023

Journal of Polytechnic

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Pneumonia Detection from Pediatric Lung X-Ray Images Using Artificial Neural Networks ABSTRACT Studies on medical imaging have grown significantly in recent years. Doctors have a crucial convenience for diagnosis thanks to semi- or fully automatic region recognition in medical imaging. It is crucial to support treatment without a specialist doctor, particularly in those nations where there is a dearth of such medical professionals. The little air sacs known as alveoli are most impacted by pneumonia, a lung inflammation. A key component of providing the right therapy conditions to heal patients and reduce harm while eradicating inflammation is early detection and precise diagnosis. Noise and blurring in patient photos obtained from X-ray machines are cleaned using deep learning algorithms and image processing techniques, and they are very helpful in. In this study, we studied chest X-ray images of pediatric patients with pneumonia and healthy individuals. XGBoost (eXtreme gradient boosting) is an innovative machine learning algorithm based on decision tree and using gradient boosting in its computations. It achieved 97.01% success with high classification performance. Keywords: Medical imaging, Machine learning, Pediatric Chest X-ray

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Yapay Sinir Ağları Kullanılarak Pediatrik Akciğer Röntgen Görüntülerinden Pnömoni Tespiti

DÜNDAR,

KOÇER

2023

Journal of Polytechnic

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Detection and Classification of Fabric Defects Using Deep Learning Algorithms

GEZE,

AKBAŞ

2024

Politeknik Dergisi

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The textile industry primarily relies on fabric as a crucial raw material, the production of which involves multiple complex stages. Due to the multitude and complexity of these stages, fabric defects can frequently occur. With the modern fabric production process being nearly fully automated, and given the variety of potential defects, detecting errors on fabrics has become increasingly challenging. The rapid pace of production and the substantial market share of the sector mean that relying on human inspection for error detection can lead to significant time losses and can reduce the accuracy of defect detection to around 60%. Consequently, recent years have seen a shift towards the development of intelligent systems for fabric defect detection in parallel with technological advancements. With the rapid progression of artificial intelligence, the application of image processing techniques has commenced in this field. This study has developed a real-time defect detection system for fabrics using deep learning techniques. Initially, a network model was created using an open-source neural network library, CNN, achieving 89% accuracy. Subsequent implementations using the VGG16 and InceptionV3 architectures reached accuracies of 89% and 86%, respectively. To further improve the study, fabrics were classified into two categories: defective and non-defective, and the pre-trained Convolutional Neural Networks model ResNet50-v2 was employed as a feature extractor. This approach yielded an approximate accuracy of 95%.

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Önceden Eği̇ti̇lmi̇ş Deri̇n Ağlar İle Göğüs Röntgeni̇ Görüntüleri̇ Kullanarak Pnömoni̇ Siniflandirilmasi

Cited by 2 publications

References 17 publications

Yapay Sinir Ağları Kullanılarak Pediatrik Akciğer Röntgen Görüntülerinden Pnömoni Tespiti

Yapay Sinir Ağları Kullanılarak Pediatrik Akciğer Röntgen Görüntülerinden Pnömoni Tespiti

Detection and Classification of Fabric Defects Using Deep Learning Algorithms

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