A novel approach in diagnosing knee osteoarthritis for content based image retrieval in big data analytics and medical images
Küçük Resim Yok
Tarih
2025
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Nature Portfolio
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
The rapid growth in database size due to technological advances has led to difficulties in locating and accessing specific data components. While deep learning and other machine learning architectures are promising in retrieving data components, their effectiveness is more pronounced when addressing groups of diseases. On the contrary, this effectiveness decreases when large data sets are accessed. Content-based Image retrieval (CBIR) methods are used in large data sets. In this study, knee osteoarthritis detection was performed using a developed hybrid CBIR-based system. Knee Osteoarthritis is the wear and tear of the cartilage in the knee joint. Knee osteoarthritis is a disease whose incidence increases, especially after a certain age. In this study, CBIR techniques were preferred to detect knee osteoarthritis. In the proposed method, feature extraction was performed using DarkNet53, Histogram of Oriented Gradients (HOG), and Local Binary Patterns (LBP). These features are combined to leverage the benefits of different aspects of the same image. To enhance the proposed model's speed and effectiveness, a hybrid model was developed utilizing the Neighborhood Component Analysis (NCA) method. Seven different distance measurement metrics were used in the developed CBIR model. Current deep learning architectures published in the literature struggle to achieve comparable success rates in distinguishing between closely related but distinct disease groups. The study highlights the challenges that increasing class diversity poses for the performance of deep learning architectures. In addition, the developed system aims to overcome the limitations of existing deep learning models in distinguishing similar disease groups.
Açıklama
Anahtar Kelimeler
Cbir, Cnn, Deep Learning, Knee Osteoarthritis, Retrieval
Kaynak
Scientific Reports
WoS Q Değeri
Q1
Scopus Q Değeri
N/A
Cilt
15
Sayı
1
