Optimized AI-Assisted Diagnosis of Spinal Anomalies Using Convolutional Neural Networks by Enhancing Feature Extraction in Small Datasets

Year 2024, Volume: 4 Issue: 2, 1 - 10, 28.08.2024

Ozan Durgut , Gökhan Bora Esmer

Abstract

Purpose: Major spinal health anomalies, particularly idiopathic scoliosis and spondylolisthesis are primarily caused by abnormal vertebral displacements. Early diagnosis is critical for effective treatment and management. However, diagnosis of these conditions requires the analysis of X-ray images by expert physicians, and when the number of patients increases, the amount of required time to have a diagnosis may take longer duration. Also, the concentration of the physician may be lost. As a result of this, physician may have an erroneous decision related to diagnosis. As a solution to the problem, we suggest a method based on artificial intelligence that helps physician to come up with the correct diagnosis.
Materials and Methods: To address the issue of insufficient datasets, we use a customized convolutional neural network model and the Leaky ReLU activation function. This approach helps us extract better features while reducing computational complexity.
Results: In our experiments, we achieve success rates of 98.51% in accuracy, 98.63% in precision, 98.53% in recall, and 98.51% in the F1 score. When we compare these results to another study using the same dataset, we see increases of 2.25% in accuracy, 1.04% in precision, 2.67% in recall, and 4.11% in the F1 score. To avoid misleading results from small or imbalanced datasets, we use a balanced version of the dataset for comparison. When we compare the model trained on the imbalanced dataset with the version trained on the balanced dataset, we find a minimal performance decrease of only 0.787% in the F1 score and an average decrease of 0.721% in the other metrics. This shows that the model performs well regardless of potential issues from dataset imbalance. We also test the model with challenging data and obtain successful metrics.
Conclusion: We achieve the objectives of increasing the success rate by reducing computational complexity and improving feature extraction for small datasets. Furthermore, experiments with challenging datasets show that our method remains generalizable and usable even on small datasets.

Keywords

Spine health, Scoliosis, Artificial intelligence, Convolutional neural network, Leaky ReLU

Küçük Veri Setlerinde Özellik Çıkarmayı Geliştirerek Evrişimli Sinir Ağları ile Omurga Anomalilerinin Yapay Zeka Destekli Teşhisinin Optimizasyonu

Abstract

Amaç: Omurga sağlığıyla ilgili başlıca anomaliler, özellikle idiopatik skolyoz ve spondilolistezis, esasen anormal vertebral kaymalar nedeniyle ortaya çıkar. Erken teşhis, etkili tedavi ve yönetim için kritiktir. Ancak, bu durumların teşhisi, X-ray görüntülerinin uzman hekimler tarafından analiz edilmesini gerektirir ve hasta sayısı arttığında teşhis süresi daha uzun olabilir. Ayrıca, hekimin dikkati dağılabilir. Bunun sonucunda, hekim teşhisle ilgili yanlış bir karar verebilir. Soruna çözüm olarak, hekime doğru teşhis koymasına yardımcı olacak yapay zeka temelli bir yöntem öneririz.
Gereç ve Yöntem: Veri seti yetersizliği sorununu ele almak için, özelleştirilmiş bir konvolüsyonel sinir ağı modeli ve Leaky ReLU aktivasyon fonksiyonunu kullanırız. Bu yaklaşım, daha iyi özellik çıkarımı yapmamıza ve hesaplama karmaşıklığını azaltmamıza yardımcı olur.
Bulgular: Deneylerimizde, doğrulukta %98.51, kesinlikte %98.63, duyarlılık %98.53 ve F1 skorunda %98.51 başarı oranları elde ediyoruz. Bu sonuçları aynı veri setini kullanan başka bir çalışma ile karşılaştırdığımızda, sırasıyla doğrulukta %2.25, kesinlikte %1.04, geri çağırmada %2.67 ve F1 skorunda %4.11 artışlar görüyoruz. Küçük veya dengesiz veri setlerinden kaynaklanabilecek yanıltıcı sonuçları önlemek için, karşılaştırma için dengelenmiş bir veri seti kullanırız. Dengesiz veri seti ile eğitilen modeli, dengelenmiş veri seti ile eğitilen versiyonu ile karşılaştırdığımızda, F1 skorunda yalnızca %0.787'lik minimal bir performans düşüşü ve diğer metriklerde ortalama %0.721'lik bir düşüş buluruz. Bu, modelin veri setinin dengesizliklerinden kaynaklanabilecek potansiyel sorunlara rağmen iyi performans gösterdiğini gösterir. Ayrıca, modeli zorlu verilerle test ediyoruz ve başarılı metrikler elde ediyoruz.
Sonuç: Hesaplama karmaşıklığını azaltarak ve küçük veri setleri için özellik çıkarımını artırarak başarı oranını artırma hedeflerini yakalarız. Ayrıca, zorlu veri setleri ile yapılan deneyler, yöntemimizin küçük veri setlerinde bile genellenebilir ve kullanılabilir olduğunu gösterir.

Keywords

Omurga sağlığı, Skolyoz, Yapay zekâ, Evrişimli sinir ağı, Sızdıran ReLU

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