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Journals / Avrupa Bilim ve Teknoloji Dergisi / 2022 / Sayı: 40

A Detection and Prediction Model Based on Deep Learning Assisted by Explainable Artificial Intelligence for Kidney Diseases

Böbrek Hastalıkları için Açıklanabilir Yapay Zeka Destekli Derin Öğrenmeye Dayalı Bir Tespit ve Tahmin Modeli

Ahmet Furkan BAYRAM
Caglar GURKAN
Abdulkadir BUDAK
Hakan KARATAŞ
Pages
67–74
Publication Date
2022-09-30
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Consequently, deep learning based xAI assisted computer aided diagnosis (CAD) system was developed for diagnosis of kidney diseases.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Kidney diseases are one of the most common diseases worldwide and cause unbearable pain in most people.
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Key Findings & Data:CT images in three classes, namely 72 kidney cysts, 394 kidney stones and 192 healthy kidneys were used in the performance analysis part of the YOLO architecture designs.
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Conclusion & Impact:Consequently, deep learning based xAI assisted computer aided diagnosis (CAD) system was developed for diagnosis of kidney diseases.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Böbrek taşı · Böbrek kisti · Derin öğrenme

Abstract

Kidney diseases are one of the most common diseases worldwide and cause unbearable pain in most people. In this study aims to detecting the cyst and stone in the kidney. For the this purpose, YOLO architecture designs were used for detection of kidney, kidney cyst and kidney stone. The YOLO architecture designs were supported by the explainable artificial intelligence (xAI) feature. CT images in three classes, namely 72 kidney cysts, 394 kidney stones and 192 healthy kidneys were used in the performance analysis part of the YOLO architecture designs. As a result, YOLOv7 architecture design outperformed the YOLOv7 Tiny architecture design. YOLOv7 architecture design achieved the mAP50 of 0.85, precision of 0.882, sensitivity of 0.829 and F1 score of 0.854. Consequently, deep learning based xAI assisted computer aided diagnosis (CAD) system was developed for diagnosis of kidney diseases.

Özet

Böbrek hastalıkları dünya çapında en yaygın hastalıklardan biridir ve çoğu insanda dayanılmaz ağrılara neden olur. Bu çalışmada böbrekteki kist ve taşın tespiti amaçlanmıştır. Bu amaçla böbrek, böbrek kisti ve böbrek taşı tespiti için YOLO mimari tasarımları kullanılmıştır. YOLO mimari tasarımları açıklanabilir yapay zeka (AYZ) özelliği ile desteklenmiştir. YOLO mimari tasarımlarının performans analizi kısmında 72 böbrek kisti, 394 böbrek taşı ve 192 sağlıklı böbrek olmak üzere üç sınıftaki BT görüntüleri kullanılmıştır. Sonuç olarak, YOLOv7 mimari tasarımı, YOLOv7 Tiny mimari tasarımından daha iyi performans gösterdi. YOLOv7 mimari tasarımı 0.85 mAP50 değerini, 0.882 kesinliği, 0.829 duyarlılığı ve 0.854 F1 skorunu elde etmiştir. Sonuç olarak, böbrek hastalıklarının teşhisi için derin öğrenme tabanlı AYZ destekli bilgisayar destekli tanı (BDT) sistemi geliştirilmiştir.

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Cite This Article

BAYRAM, A. F., GURKAN, C., BUDAK, A., KARATAŞ, H. (2022). A Detection and Prediction Model Based on Deep Learning Assisted by Explainable Artificial Intelligence for Kidney Diseases. Avrupa Bilim ve Teknoloji Dergisi, Sayı: 40, 67–74. https://ajindex.com/articles/875597

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