Informace o kvalifikační práci Detection of Diabetic Retinopathy using Deep Learning and Transfer Learning Techniques with Oversampling to Address Imbalanced Dataset
The study aims to develop a system for detecting diabetic retinopathy using deep learning. In this study I have explored transfer learning with four distinct models and addressed the issue of an unbalanced dataset with oversampling. The final experiment achieved a significant improvement in accuracy and quadratic kappa score. The study highlights the potential of deep learning and the importance of addressing dataset imbalances for accurate results.
Anotace v angličtině
The study aims to develop a system for detecting diabetic retinopathy using deep learning. In this study I have explored transfer learning with four distinct models and addressed the issue of an unbalanced dataset with oversampling. The final experiment achieved a significant improvement in accuracy and quadratic kappa score. The study highlights the potential of deep learning and the importance of addressing dataset imbalances for accurate results.
Klíčová slova
Diabetic Retinopathy, Deep learning, Transfer learning, Convolutional neural
network, Image classification, medical imaging, diabetic macular edema, retinal fundus
photographs, comparative analysis, oversampling, accuracy, quadratic kappa score
Klíčová slova v angličtině
Diabetic Retinopathy, Deep learning, Transfer learning, Convolutional neural
network, Image classification, medical imaging, diabetic macular edema, retinal fundus
photographs, comparative analysis, oversampling, accuracy, quadratic kappa score
Rozsah průvodní práce
36p.
Jazyk
AN
Anotace
The study aims to develop a system for detecting diabetic retinopathy using deep learning. In this study I have explored transfer learning with four distinct models and addressed the issue of an unbalanced dataset with oversampling. The final experiment achieved a significant improvement in accuracy and quadratic kappa score. The study highlights the potential of deep learning and the importance of addressing dataset imbalances for accurate results.
Anotace v angličtině
The study aims to develop a system for detecting diabetic retinopathy using deep learning. In this study I have explored transfer learning with four distinct models and addressed the issue of an unbalanced dataset with oversampling. The final experiment achieved a significant improvement in accuracy and quadratic kappa score. The study highlights the potential of deep learning and the importance of addressing dataset imbalances for accurate results.
Klíčová slova
Diabetic Retinopathy, Deep learning, Transfer learning, Convolutional neural
network, Image classification, medical imaging, diabetic macular edema, retinal fundus
photographs, comparative analysis, oversampling, accuracy, quadratic kappa score
Klíčová slova v angličtině
Diabetic Retinopathy, Deep learning, Transfer learning, Convolutional neural
network, Image classification, medical imaging, diabetic macular edema, retinal fundus
photographs, comparative analysis, oversampling, accuracy, quadratic kappa score
Zásady pro vypracování
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Zásady pro vypracování
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Seznam doporučené literatury
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Seznam doporučené literatury
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Přílohy volně vložené
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Přílohy vázané v práci
ilustrace, tabulky
Převzato z knihovny
Ne
Plný text práce
Přílohy
Posudek(y) oponenta
Hodnocení vedoucího
Záznam průběhu obhajoby
Committee: Konvička, Regl, Vohnout, Vohnoutova
The student has presented her thesis within the time given.
Questions:
- Why did you not communicated well the experiment designs with your supervisor?
- What if the retina is damaged from different diseases (not only diabetes) ? Are you able to detect it?
- Your score is about 80%. What if you compare this to the accuracy of the doctor?