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

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