Development of Segmentation and Classification Algorithms for Computed Tomography Images of Human Kidney Stone

Abstract

Computed tomography (CT) scan diagnostics procedures adopt the use of image information retrieval system with the help of radiographer’s expertise. However, this technique is prone to errors. Significant height of accuracy is required in healthcare decision support, as 20% of CT scans are associated with error. The application of artificial intelligence (AI) can improve performance level, mitigate human error, and enhance clinical decision support in the context of time and accuracy. The study introduced machine learning algorithm to analyze stream of anonymous CT scans of kidney. The research adopted deep learning approach for segmentation and classification of kidney stone (renal calculi) images in Python (with Keras and TensorFlow) environment. A control volume of data along with 336 kidney stone images were used to train the deep learning network with 10 testing images. The training images were divided into two sets (folders) as follows; one was labeled as STONE (containing 167 images) and the other as NO-STONE (containing 169 images); 10 iterations were performed for model training. The network layers were structured as input layer in the following with 2-D convolutional neural network machine learning (CNN-ML), ReLU activation, Maxpooling, and fully connected (dense) layer including the sigmoid activation layer. The training adopted a batch size of 8 with 10% validation. The output result, upon testing the model, has an accuracy of 90%, sensitivity value of 80% and effectiveness of 89%. The segmentation and classification algorithm model could be embedded in future CT diagnostic procedure to enhance medical decision support and accuracy.

Automatic Kidney Stone Detection using Low-cost CNN with Coronal CT Images

“…The authors trained a CNN model with 349 CT scans and tested on 88 scans, obtaining a specificity of 1.0 and an F1-score of 0.783 on their best model. [18] also used CNN for kidney stone detection, obtaining an accuracy of 90%, a sensitivity of 80%, and a F1score of 89%. [19] explored CNN in different planes of CT images.…”