Abstract:
Kidney stone illness has been a severe reoccurring issue observed more frequently all over the world. By
the other name “Nephrolithiasis” a deposition of crystallized minerals, acids and proteins found in urine
within the kidneys. These stones can be anywhere between <3mm up to >20 mm in length, causing hurt
and injury to the body because of their size and shape. Diagnosis of kidney stones is done using a variety
of imaging techniques, such as Computed Tomography (CT), X-ray and Ultrasound. Since, CT and X-ray
methods are expensive, expose patients to radiation and due to limited access. Clinical centers in Ethiopia
prefer ultrasound imaging approach for the patients. However, the inescapable roughness and speckles
of ultrasonic images make it difficult, leading to erroneous diagnosis of smaller kidney stones and im pairing their treatment. In this study, a deep learning algorithm and despeckling techniques have been
used to accurately estimate and detect kidney stones from ultrasound images. In total, a set of 135 normal
and 495 kidney stone images have been acquired from Jimma University Medical Center and Worabe
Comprehensive Specialized Hospital and used for training, validation and testing purposes. The acquired
images were preprocessed and despeckled using image cropping and resizing, normalized, filtered using
adaptive wiener filter, median filter and equalized using histogram equalization and the performance of
each despeckling technique was evaluated. Also, augmentation techniques has been used for increasing
the number of data sets after partitioning of dataset. Augmented train split data sets were trained on Yolo Nas, Yolov8, Yolov5 and Detectron2 Faster RCNN models. Each model trained first using pre-trained
COCO data set rather than training from scratch. Then models were evaluated through valid and test
datasets in the training and inference image respectively. By adjusting the hyper-parameters through trial
and error of the neural network during training the execution of each model were examined and improved.
Overall, the study anticipated a precise Nephrolithiasis diagnostic tool that provides reliable decision making support to the healthcare professionals ensuring patient safety. In this regard, our developed
model using Yolo-Nas architecture has the highest accuracy and mAP scores of 94% and 86.7%, respec tively, in detecting the stones.
