Improved Detection of Urolithiasis Using High-Resolution Computed Tomography Images by a Vision Transformer Model

Abstract

Purpose: Urinary stones cause lateral abdominal pain and are a prevalent condition among younger age groups. The diagnosis typically involves assessing symptoms, conducting physical examinations, performing urine tests, and utilizing radiological imaging. Artificial intelligence models have demonstrated remarkable capabilities in detecting stones. However, due to insufficient datasets, the performance of these models has not reached a level suitable for practical application. Consequently, this study introduces a vision transformer (ViT)-based pipeline for detecting urinary stones, using computed tomography images with augmentation.Methods: The super-resolution convolutional neural network (SRCNN) model was employed to enhance the resolution of a given dataset, followed by data augmentation using CycleGAN. Subsequently, the ViT model facilitated the detection and classification of urinary tract stones. The model’s performance was evaluated using accuracy, precision, and recall as metrics.Results: The deep learning model based on ViT showed superior performance compared to other existing models. Furthermore, the performance increased with the size of the backbone model.Conclusions: The study proposes a way to utilize medical data to improve the diagnosis of urinary tract stones. SRCNN was used for data preprocessing to enhance resolution, while CycleGAN was utilized for data augmentation. The ViT model was utilized for stone detection, and its performance was validated through metrics such as accuracy, sensitivity, specificity, and the F1 score. It is anticipated that this research will aid in the early diagnosis and treatment of urinary tract stones, thereby improving the efficiency of medical personnel.

Enhancing Automated Brain Tumor Detection Accuracy Using Artificial Intelligence Approaches for Healthcare Environments

“…Indeed, the proposed model may encounter challenges in accurately detecting small brain tumors, as deep learning models heavily rely on the training images for learning. To address this limitation and enhance the model’s performance, future improvements can be achieved through the creation of a dedicated dataset comprising small brain tumor images [ 65 , 66 , 67 , 68 , 69 , 70 ]. By assembling a comprehensive dataset specifically focused on small brain tumors, we can expose the model to a diverse array of such cases, enabling it to better discern the subtle characteristics and intricate patterns associated with these tumors.…”