A K-nearest neighbors-based classification approach for automated detection of knee osteoarthritis

Çağlar Cengizler; Ayşe Gül Kabakcı

Makale detayları

A K-nearest neighbors-based classification approach for automated detection of knee osteoarthritis

Yazarlar:

Çağlar Cengizler | Ayşe Gül Kabakcı

Yayıncı:

Cukurova University

Ana Başlıklar:

Klinik Tıp Bilimleri

Dergi:

Cukurova Medical Journal Vol.48 Issue 2, pp.715-722

Belge Tipi:

Makale

Yayın Tarihi:

2/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001286380

pISSN:

2602-3032

Anahtar kelimeler:

Özet:

 Purpose: Osteoarthritis is a serious condition that can significantly reduce a persons quality of life, causing pain and stiffness in the knees and limiting their mobility. The condition progressively worsens over time, emphasizing the importance of early diagnosis. This study implemented a computer-aided classification approach to reduce the time and effort required for diagnosing knee osteoarthritis while minimizing human errors. Materials and Methods: Data analyzed in this study was obtained from the Osteoarthritis Initiative. A total of 165 samples were used in the study. All abnormal samples were graded as severe osteoarthritis. While 78 samples were used to test the implemented algorithm, the training process of the algorithm was completed with 87 samples. The proposed approach involves three main stages: segmenting the cartilage region through a series of image-processing operations, extracting morphological features from the defined region, and classifying samples based on these features. In the classification stage, morphological features characterizing the cartilage region were classified in the observation space, and the k-nearest neighbors algorithm was applied for automated discrimination. Accordingly, the computer utilizes the previously classified sample features to estimate the presence of pathology. Results: Test classifications were completed with 78 samples; 28 were previously diagnosed with osteoarthritis. Morphological measures of the training samples were accepted as a reference for abnormality. The applied classification scheme can distinguish severed cartilage regions with a 0.95 accuracy. Conclusion: This study demonstrates the potential effectiveness of a computer-aided approach in diagnosing knee osteoarthritis with high accuracy. The developed approach offers a promising solution for early and efficient diagnosis, enabling more timely and effective treatment strategies for osteoarthritis patients. The progressive nature of the disease makes these advancements in diagnostic methods invaluable. Future studies may focus on expanding the sample size and further refining the model for enhanced precision and broad applicability in clinical settings.

 Purpose: Osteoarthritis is a serious condition that can significantly reduce a persons quality of life, causing pain and stiffness in the knees and limiting their mobility. The condition progressively worsens over time, emphasizing the importance of early diagnosis. This study implemented a computer-aided classification approach to reduce the time and effort required for diagnosing knee osteoarthritis while minimizing human errors. Materials and Methods: Data analyzed in this study was obtained from the Osteoarthritis Initiative. A total of 165 samples were used in the study. All abnormal samples were graded as severe osteoarthritis. While 78 samples were used to test the implemented algorithm, the training process of the algorithm was completed with 87 samples. The proposed approach involves three main stages: segmenting the cartilage region through a series of image-processing operations, extracting morphological features from the defined region, and classifying samples based on these features. In the classification stage, morphological features characterizing the cartilage region were classified in the observation space, and the k-nearest neighbors algorithm was applied for automated discrimination. Accordingly, the computer utilizes the previously classified sample features to estimate the presence of pathology. Results: Test classifications were completed with 78 samples; 28 were previously diagnosed with osteoarthritis. Morphological measures of the training samples were accepted as a reference for abnormality. The applied classification scheme can distinguish severed cartilage regions with a 0.95 accuracy. Conclusion: This study demonstrates the potential effectiveness of a computer-aided approach in diagnosing knee osteoarthritis with high accuracy. The developed approach offers a promising solution for early and efficient diagnosis, enabling more timely and effective treatment strategies for osteoarthritis patients. The progressive nature of the disease makes these advancements in diagnostic methods invaluable. Future studies may focus on expanding the sample size and further refining the model for enhanced precision and broad applicability in clinical settings.

 Purpose: Osteoarthritis is a serious condition that can significantly reduce a persons quality of life, causing pain and stiffness in the knees and limiting their mobility. The condition progressively worsens over time, emphasizing the importance of early diagnosis. This study implemented a computer-aided classification approach to reduce the time and effort required for diagnosing knee osteoarthritis while minimizing human errors. Materials and Methods: Data analyzed in this study was obtained from the Osteoarthritis Initiative. A total of 165 samples were used in the study. All abnormal samples were graded as severe osteoarthritis. While 78 samples were used to test the implemented algorithm, the training process of the algorithm was completed with 87 samples. The proposed approach involves three main stages: segmenting the cartilage region through a series of image-processing operations, extracting morphological features from the defined region, and classifying samples based on these features. In the classification stage, morphological features characterizing the cartilage region were classified in the observation space, and the k-nearest neighbors algorithm was applied for automated discrimination. Accordingly, the computer utilizes the previously classified sample features to estimate the presence of pathology. Results: Test classifications were completed with 78 samples; 28 were previously diagnosed with osteoarthritis. Morphological measures of the training samples were accepted as a reference for abnormality. The applied classification scheme can distinguish severed cartilage regions with a 0.95 accuracy. Conclusion: This study demonstrates the potential effectiveness of a computer-aided approach in diagnosing knee osteoarthritis with high accuracy. The developed approach offers a promising solution for early and efficient diagnosis, enabling more timely and effective treatment strategies for osteoarthritis patients. The progressive nature of the disease makes these advancements in diagnostic methods invaluable. Future studies may focus on expanding the sample size and further refining the model for enhanced precision and broad applicability in clinical settings.

 Amaç: Osteoartrit, kişinin yaşam kalitesini düşüren, dizlerde hissedilen ağrı ve sertlik ile kişinin hareket kabiliyetini kısıtlayabilen ve zamanla şiddetini arttıran ciddi bir rahatsızlıktır. Hastalığın ilerleyici karakteri erken tanının önemini artırmaktadır. Röntgen görüntüleri bu hastalığım teşhisi için klinisyenler tarafından en çok tercih edilen araçlardan biridir. Çalışmada bilgisayar destekli sınıflandırma yaklaşımı ile diz osteoartritinin teşhisi için gereken zaman ve iş gücünün azaltılarak insan kaynaklı hataların minimize edilmesi hedeflenmiştir. Gereç ve Yöntem: Bu çalışmada analiz edilen veriler, Osteoartrit Girişiminden elde edilmiştir. Çalışmada toplamda 165 örnek kullanılmıştır. Tüm anormal örneklerde şiddetli kıkırdak zararı gözlenmiştir. Uygulanan algoritmanın test safhasında 78, eğitim aşamasında ise 87 örnek kullanılmıştır. Önerilen yaklaşım üç ana aşama içermektedir: kıkırdak bölgesinin görüntü işleme yöntemleri ile bölütlenmesi, sınırları çizilen bölgeden morfolojik özelliklerin çıkarılması ve bu özelliklerin değerlendirilerek örneklerin sınıflandırılması. Sınıflandırma aşamasında, gözlem uzayı kıkırdak bölgesini karakterize eden morfolojik özellikler ile oluşturulmuş, otomatik sınıflandırma işlemi için k-en yakın komşu algoritması uygulanmıştır. Buna göre, bilgisayar önceden sınıflandırılmış örnek özelliklerini kullanarak patolojinin varlığını tahmin etmektedir. Bulgular: Test sınıflandırmaları 28ine osteoartrit teşhisi konmuş toplam 78 örnekle tamamlanmıştır. Eğitim örneklerinin sayısal özellikleri kıkırdak hasarının otomatik tespiti için referans olarak kabul edilmiştir. Uygulanan sınıflandırma mekanizması yıpranmış kıkırdak bölgelerini 0.95 doğrulukla ayırt edebilmiştir. Sonuç: Hastalığın ilerleyici doğası, teşhis yöntemlerindeki ilerlemeleri oldukça değerli kılmaktadır. Sonraki çalışmalar örneklem büyüklüğünün genişletilerek ümit vadeden modelin klinik ortamlarda yüksek doğrulukla uygulanabilirlik kazanması için geliştirilmesine odaklanabilir.

Makale detayları

A K-nearest neighbors-based classification approach for automated detection of knee osteoarthritis

Yazarlar:

Çağlar Cengizler | Ayşe Gül Kabakcı

Yayıncı:

Cukurova University

Ana Başlıklar:

Klinik Tıp Bilimleri

Dergi:

Cukurova Medical Journal Vol.48 Issue 2, pp.715-722

Belge Tipi:

Makale

Yayın Tarihi:

2/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001286380

pISSN:

2602-3032

automated

classification

knee

osteoarthritis

morphometry

otomatik

sınıflandırma

diz

osteoartrit

morfometri

Sayı

Makale detayları

Yazarlar: Çağlar Cengizler | Ayşe Gül Kabakcı

Yayıncı: Cukurova University

Ana Başlıklar: Klinik Tıp Bilimleri

Dergi: Cukurova Medical Journal Vol.48 Issue 2, pp.715-722

Belge Tipi: Makale

Yayın Tarihi: 2/2023

Belge Dili: İngilizce

Document ID (Org No.): 2001286380

pISSN: 2602-3032

Sayı

Yazarlar:

Çağlar Cengizler | Ayşe Gül Kabakcı

Yayıncı:

Cukurova University

Ana Başlıklar:

Klinik Tıp Bilimleri

Dergi:

Cukurova Medical Journal Vol.48 Issue 2, pp.715-722

Belge Tipi:

Makale

Yayın Tarihi:

2/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001286380

pISSN:

2602-3032