Study on deep learning-based detection of viable cell count in dialysis fluid images

Abstract

Abstract Background and aims Control of dialysate fluid quality is critical to secure the safety of dialysis treatment. The number of colonies is manually counted when determining viable cell count in dialysis fluid, but errors and subjective interpretation on the part of the measurer can be problematic. This prompted us to examine the potential for using deep learning to detect viable cells and count their numbers. Methods In this study we prepared 5360 images for detecting viable cell count and classified them into four categories using the VGG-16 model. These images were resized to 224 × 224 px; 90% of them were used for learning, and 10% were used for validation. In an alternative approach, we also created 110 annotated images from images to detect viable cell count in dialysis fluid and subjected them to learning using the YOLOv5 model. Results VGG-model had a detection accuracy using the test data was 43%. YOLOv5 model had a mAP (Mean Average Precision) was 0.842. The detection accuracy using the test data was 90%. Conclusions The method using the VGG-16 model had problems with overfitting, suggesting that the model was not sufficiently expressive. The detection of viable cells using the YOLOv5 model showed high accuracy.