COVID-19 diagnosis with artificial intelligence algorithms
Başlık çevirisi mevcut değil.
- Tez No: 826129
- Danışmanlar: DR. ÖĞR. ÜYESİ MESUT ÇEVİK
- Tez Türü: Yüksek Lisans
- Konular: Elektrik ve Elektronik Mühendisliği, Electrical and Electronics Engineering
- Anahtar Kelimeler: Belirtilmemiş.
- Yıl: 2023
- Dil: İngilizce
- Üniversite: Altınbaş Üniversitesi
- Enstitü: Lisansüstü Eğitim Enstitüsü
- Ana Bilim Dalı: Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı
- Bilim Dalı: Belirtilmemiş.
- Sayfa Sayısı: 79
Özet
When the covid-19 virus initially spread globally in 2019, As a result, numerous people passed away. Due to this sickness, medical staff members are busier than ever. For reducing this load, computer-aided diagnostic tools and machine learning will assist in some ways. Any scientific investigation into this disease can help it be eliminated rapidly. In our study, we utilized the Covid-19 dataset which is publicly accessible (14486 cases) from the Kaggle website. Training and test data categories were created from the dataset, using 80% of the data for training and 20% for testing. This research has two main stages: First, we extract Visual Geometry Group (VGG16) features from the dataset, then based on these extracted features, five machine learning algorithms were employed for classification (k-nearest neighbor (KNN), random forest (RF), extreme gradient boosting (XGBoost), decision trees (DT), and support vector machine (SVM)). Second, we extract gray-level co-occurrence matrix (GLCM) features from the dataset. Then, we applied the same five machine learning algorithms (DT, RF, KNN, XGBoost, and SVM) for classification based on these extracted features. Performance metrics were examined utilizing a confusion matrix, accuracy, recall, and precision together with the F1 score.Regarding classification accuracy, the Support vector machine algorithm and VGG16 were the classification techniques that excel at 98.22% accuracy and achieve the impressively rapid and best result.
Özet (Çeviri)
When the covid-19 virus initially spread globally in 2019, As a result, numerous people passed away. Due to this sickness, medical staff members are busier than ever. For reducing this load, computer-aided diagnostic tools and machine learning will assist in some ways. Any scientific investigation into this disease can help it be eliminated rapidly. In our study, we utilized the Covid-19 dataset which is publicly accessible (14486 cases) from the Kaggle website. Training and test data categories were created from the dataset, using 80% of the data for training and 20% for testing. This research has two main stages: First, we extract Visual Geometry Group (VGG16) features from the dataset, then based on these extracted features, five machine learning algorithms were employed for classification (k-nearest neighbor (KNN), random forest (RF), extreme gradient boosting (XGBoost), decision trees (DT), and support vector machine (SVM)). Second, we extract gray-level co-occurrence matrix (GLCM) features from the dataset. Then, we applied the same five machine learning algorithms (DT, RF, KNN, XGBoost, and SVM) for classification based on these extracted features. Performance metrics were examined utilizing a confusion matrix, accuracy, recall, and precision together with the F1 score.Regarding classification accuracy, the Support vector machine algorithm and VGG16 were the classification techniques that excel at 98.22% accuracy and achieve the impressively rapid and best result.
Benzer Tezler
- Yapay zeka teknikleri ile COVID-19 hastalık tahmini
COVID-19 disease prediction with artificial intelligence techniques
ABDULLAH TÜRKER TOKU
Yüksek Lisans
Türkçe
2023
Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve KontrolKarabük ÜniversitesiBilgisayar Mühendisliği Ana Bilim Dalı
DR. ÖĞR. ÜYESİ FERHAT ATASOY
- Enhance the performance of preprocessing techniques by using artificial intelligence algorithms
Yapay zeka algoritmaları kullanarak ön işleme tekniklerinin performansını artırın
HUMAM QUTAIBA ABDULRAHMAN AL-DOORI
Yüksek Lisans
İngilizce
2022
Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve KontrolAltınbaş ÜniversitesiElektrik ve Bilgisayar Mühendisliği Ana Bilim Dalı
YRD. DOÇ. DR. ABDULLAHİ ABDUL IBRAHIM
- Yapay zeka tabanlı akciğer röntgen görüntülerinden covıd-19 tespiti
Covid-19 detection from artificial intelligence based lung x-ray images
ÖZGÜR KART
Yüksek Lisans
Türkçe
2022
Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve KontrolSelçuk ÜniversitesiBilgisayar Mühendisliği Ana Bilim Dalı
PROF. DR. FATİH BAŞÇİFTÇİ
- Sunucu tabanlı yapay zekalı COVID-19 belirtileri analizi yapan robot
Server-based artificial intelligence robot analyzing COVID-19 symptoms
UĞUR YILDIRIM
Yüksek Lisans
Türkçe
2022
Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve KontrolAfyon Kocatepe ÜniversitesiBilgisayar Ana Bilim Dalı
PROF. DR. ÖMER DEPERLİOĞLU
- Deep feature transfer from deep learning models into machine learning algorithms to classify COVID-19 from chest X-ray images
Göğüs röntgeni görüntülerinden COVID-19 sınıflandırması yapmak amacıyla derin öğrenme modellerinden makine öğrenmesi algoritmalarına derin öznitelik aktarımı
OZAN GÜLDALİ
Yüksek Lisans
İngilizce
2021
Matematikİstanbul Teknik ÜniversitesiMatematik Mühendisliği Ana Bilim Dalı
DR. ÖĞR. ÜYESİ GÜL İNAN