Deep learning has emerged as a potent tool for various tasks, such as image classification. However, in the medical domain, there exists a scarcity of data, which poses a challenge in obtaining a well-balanced and high-quality dataset. Commonly seen issues in the realm of renal health include conditions such as kidney stones, cysts, and tumors. This study is centered on the examination of deep learning models for the purpose of classifying renal computed tomography (CT)-scan pictures. State-of-the-art classification models, such as convolutional neural network (CNN) approaches, are employed to boost model performance and improve accuracy. The algorithm is comprised of six convolutional layers that progressively increase in complexity. Every layer in the network utilizes a uniform 3x3 kernel size and applies the rectified linear unit (ReLU) activation function. This is followed by a max-pooling layer that downsamples the feature maps using a 2x2 pool size. Following this, a flatten layer was implemented in order to preprocess the data for the fully linked layers. The consistent utilization of uniform kernel sizes and activation functions throughout all layers of the model facilitated the smooth extraction of complex features, thereby enhancing the model’s ability to accurately identify different kidney conditions. As a result, we achieved a high accuracy rate of 99.8%, precision is 99.8%, and F1 score of approximately 99.7%.
AlMahadin et al. (2024) studied this question.