Deep convolutional generative adversarial networks in retinitis pigmentosa disease images augmentation and detection

• Autorzy: Powroźnik Paweł , Skublewska-Paszkowska Maria , Nowomiejska Katarzyna , Aristidou Andreas , Panayides Andreas , Rejdak Robert

Identyfikatory

DOI

10.12913/22998624/196179

• Języki publikacji: EN

Abstrakty

EN

Large medical datasets are crucial for advancing contemporary medical practices that incorporate computer vision and machine learning techniques. These records serve as indispensable resources for identifying patterns that assist healthcare professionals in diagnosing rare diseases and enhancing patient outcomes. Moreover, these datasets drive research into the causes and progression of such diseases, potentially leading to innovative therapeutic strategies. However, the acquisition of such data poses significant challenges due to privacy and ethical concerns, as well as the rarity of certain conditions. Therefore, it is imperative to both collect new medical data and develop tools that facilitate the enhancement of existing datasets while preserving the accurate characteristics of the diseases. This study focuses on leveraging Deep Convolutional Generative Adversarial Networks (DCGAN) to expand a dataset containing images of retinitis pigmentosa, a rare eye condition affecting the retina. Our study showcases that integrating Xtreme Gradient Boosting (XGBoost) within the DCGAN framework enhances the clarity and quality of these augmented images. By employing hybrid VGG16 alongside XGBoost techniques during training, we observe improvements in detection accuracy. The outcomes of the proposed method are highly promising, with the model achieving all key performance metrics surpassing the 90% threshold as well as improving baseline classification accuracy by almost 19%.

Słowa kluczowe

EN

generative adversarial network; image augmentation; medical image augmentation; rare eye disease classification; eye diseases; retinitis pigmentosa

• Wydawca: Lublin University of Technology ; Polish Society of Ecological Engineering (PTIE), Branch of PTIE in Lublin
• Czasopismo: Advances in Science and Technology. Research Journal
• Rocznik: 2025
• Tom: Vol. 19, no. 2
• Strony: 321--340
• Opis fizyczny: Bibliogr. 65 poz., fig., tab.

Twórcy

autor

Powroźnik Paweł

• Department of Computer Science, Lublin University of Technology, Nadbystrzycka 36B, 20-618 Lublin, Poland

• https://orcid.org/0000-0002-5705-4785

autor

Skublewska-Paszkowska Maria

• Department of Computer Science, Lublin University of Technology, Nadbystrzycka 36B, 20-618 Lublin, Poland

• https://orcid.org/0000-0002-0760-7126

autor

Nowomiejska Katarzyna

• Chair and Department of General and Pediatric Ophthalmology, Medical University of Lublin, Chmielna 1, 20-400 Lublin, Poland

• https://orcid.org/0000-0002-5805-8761

autor

Aristidou Andreas

• University of Cyprus, 75 Kallipoleos Str., Nicosia 1678, Cyprus
• CYENS Centre of Excellence, 23 Dimarchias Square, Nicosia 1016, Cyprus

• https://orcid.org/0000-0003-3321-2723

autor

Panayides Andreas

• CYENS Centre of Excellence, 23 Dimarchias Square, Nicosia 1016, Cyprus

• https://orcid.org/0000-0003-3321-2723

autor

Rejdak Robert

• Chair and Department of General and Pediatric Ophthalmology, Medical University of Lublin, Chmielna 1, 20-400 Lublin, Poland

• https://orcid.org/0000-0003-3321-2723

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