Effective Kidney Stone Prediction Based on Optimized Yolov7 Segmentation and Deep Learning Classification
Keywords:
Kidney stone, Image Segmentation, YOLOv7 model, Energy Valley optimizer, PCNN classification, performance metricsAbstract
Recently, the most common urological disease is Kidney stones that affect most of the individuals globally which provides a severe pain and discomfort. The kidney stone prediction is must for effective treatment planning and patient care. In this work, the Deep learning model of YOLO v7 model based Energy Valley optimizer for image segmentation and Pulse Couple Neural Network (PCNN) based classification is proposed effective approach for kidney stone prediction. At first, the YOLO v7 model is identified and localized the kidney stones for image segmentation. However, to provide an optimal result in YOLOv7 performance, the hyperparameter tunings are performed to learn and generalize from data. To achieve an optimal result, the Energy Valley optimizer is introduced that is motivated by energy valleys found in physics. This optimizer efficiently searches for optimal hyperparameters, mitigating issues such as local optima and slow convergence. By combining the YOLO v7 model with the Energy Valley optimizer, it enhances the model’s predictive capabilities and improves an accurate segmentation of kidney stones. Furthermore, the Pulse Couple Neural Network (PCNN) method is presented classification framework to classify kidney stones based on their attributes. The PCNN model leverages the temporal dynamics of pulse-coupled oscillators to capture complex patterns and relationships within the segmented kidney stone regions. This facilitates accurate classification into different stone types, aiding in personalized treatment planning. In experiment, the proposed technique has achieved remarkable results across various evaluation metrics such as precision, recall, accuracy, F1 score and specificity values of 98.58%, 99.17%, 98.88%, 97.42%, and 98.23% respectively. These metrics demonstrated exceptional accuracy in detecting and classifying kidney stones than the other traditional techniques. The proposed model has validated the effectiveness and superiority of the proposed technique for kidney stone prediction.
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