Comparison of Lightweight and Traditional CNN Architectures in COVID-19 Detection from Lung X-Ray Images

ERYILMAZ, Furkan; Karacan, Hacer

doi:10.29130/dubited.1011829

Düzce Üniversitesi Bilim Ve Teknoloji Dergisi

2021

DOI: 10.29130/dubited.1011829

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Comparison of Lightweight and Traditional CNN Architectures in COVID-19 Detection from Lung X-Ray Images

Furkan ERYILMAZ

Hacer Karacan

Abstract: Solunum sistemine etki eden ve ileri vakalarda ölüme neden olan korona virüs salgını yaklaşık iki yıldır devam etmektedir. Her ülkenin salgın ile mücadele yöntemi farklı olmasına rağmen ortak izlenen metot ise hastalığın tespiti ve izolasyonudur. Tespit ve izolasyon için en kritik adım ise COVID-19 tanısının doğru ve hızlı konulmasıdır. Akciğer X-Ray görüntülerinde virüse özgü bulgulara rastlanılması, bu verilerin hastalık teşhisinde kullanılabileceğini göstermektedir. İlgili çalışmanın amacı, makine öğrenmesi… Show more

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Cited by 4 publications

References 30 publications

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Covid19 Yayilimini Azaltmak İçin Yüz Maskesinin Evrişimsel Sinir Aği Modelleri ile Tespiti

DAŞGIN,

ADEM,

KILIÇARSLAN

2023

Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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Son yıllarda hayatımızın gerçeği olan ve tüm dünya için pandemi haline gelen Covid-19’un gerekli tedbirlere sıkı şekilde uyulmadığı takdirde bulaşma oranı artmakta hatta varyantları bile ortaya çıkmaya başlamaktadır. DSÖ tarafından yayınlanan ve alınması gerekli olan tedbirler alındıkça hastalıkla mücadele daha kolay hale gelebilmektedir. Tedbirlere uymanın zorluğu olsa da uymaya özen gösterildiği taktirde, hastalık ya daha hafif atlatılmakta ya da hastalığa kolayca yakalanılmamaktadır. Bu tedbirlerin en önemlilerinden birisi de kalabalık alanlarda maske kullanımına özen gösterilmesidir. Maske kullanımının önemi araştırmalarla desteklenmesinden sonra, bazı alışveriş merkezi, sağlık kuruluşları, okullar gibi kalabalık mekanlarda maske kullanımına yönelik denetimler başlamıştır. Ancak bu denetimleri bir insanın gerçekleştirmesi zor olduğundan günümüzde sıklıkla kullanılan derin öğrenme yöntemleriyle maske tespiti çalışmaları yapılmaya başlanmıştır. Bu tez çalışmasında, transfer öğrenme tabanlı modeller kullanılarak maske tespiti gerçekleştirilmesi amaçlanmaktadır. Kaggle web sitesinde bulunan veri seti ile toplamda 906 görüntü ile DenseNet121, EfficientNetV2M, NasNetMobile, InceptionV3, VGG19 ve InceptionResNetV2 derin öğrenme modelleri kullanılmıştır. Deneysel değerlendirmeler sonucunda, en iyi başarı oranı olarak NasNetMobile modeliyle, %99.35 doğruluk, %99 kesinlik, %99 geri çağırma ve %99 f1 skorları elde edildiği görülmüştür.

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Covid19 Yayilimini Azaltmak İçin Yüz Maskesinin Evrişimsel Sinir Aği Modelleri ile Tespiti

DAŞGIN,

ADEM,

KILIÇARSLAN

2023

Iğdır Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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Detection of Papilledema Severity from Color Fundus Images using Transfer Learning Approaches

KOKULU,

GÖKER

2023

Aksaray University Journal of Science and Engineering

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Papilledema is edema in the area where the optic nerve meets the eye as a result of increased pressure inside the head. This disease can result in very serious problems, such as abnormal optical changes, decreased visual acuity, and even permanent blindness if left untreated. In this study, an image processing based solution was presented for the detection of papilledema severity from color fundus images using transfer learning approaches. The image dataset includes 295 papilledema images, 295 pseudopapilledema images, and 779 control images. Histogram equalization and the 3D box filter were used for image preprocessing. The images were enhanced with the histogram equalization method and denoised with the 3D box filter method. Then, the performances of EfficentNet-B0, GoogLeNet, MobileNetV2, NASNetMobile, and ResNet-101 transfer learning approaches were compared. The hold-out method was used to calculate the performance of transfer learning. In the experiments, the MobileNetV2 approach had the highest performance with 0.96 overall accuracy and 0.94 Cohen's Kappa. The results of the experiments proved that the combination of the histogram equalization, the 3D box filter, and the MobileNetV2 transfer learning approach can be used for automatic detection of papilledema severity. Compared to other similar studies that are known in the literature, the overall accuracy was higher.

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Transfer Learning for Detection of Casting Defects Model In Scope of Industrial 4.0

TANYILDIZ,

BATUR ŞAHİN

2023

Türk Doğa Ve Fen Dergisi

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Casting represents a production process where a liquid material is poured into a mold with a hollow cavity, usually of the intended shape, following which its solidification is allowed. Numerous defect types are available, including blow holes, pin holes, burrs, mold material defects, shrinkage defects, metallurgical defects, casting metal defects, etc. All industries have quality control departments to eliminate the occurrence of this defective product. But the main problem is that this inspection process is done manually. This is a very time consuming process and due to human sensitivity this is not 100% accurate. In this study, we will verify whether the "manual inspection" bottleneck can be eliminated by automating the inspection process with transfer learning in the manufacturing process of casting products. In this study, we will verify whether the "manual inspection" bottleneck can be eliminated by automating the inspection process with transfer learning in the manufacturing process of casting products. In this study, the casting images were divided into two separate classes, and the classification process was carried out by applying deep learning architectures. The benefits of this proposed approach are discussed and proposed as a more efficient way to control the quality of final products under Industry 4.0.

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Comparison of Lightweight and Traditional CNN Architectures in COVID-19 Detection from Lung X-Ray Images

Cited by 4 publications

References 30 publications

Covid19 Yayilimini Azaltmak İçin Yüz Maskesinin Evrişimsel Sinir Aği Modelleri ile Tespiti

Covid19 Yayilimini Azaltmak İçin Yüz Maskesinin Evrişimsel Sinir Aği Modelleri ile Tespiti

Detection of Papilledema Severity from Color Fundus Images using Transfer Learning Approaches

Transfer Learning for Detection of Casting Defects Model In Scope of Industrial 4.0

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