Traditionally blood cells are counted manually using haemocytometer along with other laboratory equipment's and chemical compounds, which is a time-consuming and tedious task. This models tries to solve these drawbacks by using a machine learning based approach to positively identify and count three types of blood cells. With the development of machine learning techniques, image classification and object detection applications are becoming more robust and more accurate. As a result, machine learning based methods are being applied in different fields. Particularly, deep learning methods are being applied in different medical applications such as abnormality detection in X-rays and detection of diabetic retinopathy in retinal fundus photographs to name a few . This machine learning approach for automatic identification and counting of three types of blood cells utilizes ‘you only look once’ (YOLO) object detection and classification algorithm. YOLO framework has been trained with a modified configuration Blood Cell Count Dataset of blood smear images to automatically identify and count red blood cells, white blood cells, and platelets. Moreover, this study with other convolutional neural network architectures considering architecture complexity, reported accuracy, and running time with this framework and compare the accuracy of the models for blood cells detection. They also tested the trained model on smear images from a different dataset and found that the learned models are generalized. Overall the computer-aided system of detection and counting enables us to count blood cells from smear images in less than a second, which is useful for practical applications.
Input variables : Blood smear images
Output Variables : Produce count of blood cells and location
| Statistical | : | Somers D | Accuracy | Precision and Recall | Confusion Matrix | F1 Score | Roc and Auc | Prevalence | Detection Rate | Balanced Accuracy | Cohen's Kappa | Concordance | Gini Coefficent | KS Statistic | Youden's J Index |
| Infrastructure | : | Log Bytes | Logging/User/IAMPolicy | Logging/User/VPN | CPU Utilization | Memory Usage | Error Count | Prediction Count | Prediction Latencies | Private Endpoint Prediction Latencies | Private Endpoint Response Count |
Visit Model : github.com
Additional links : ncbi.nlm.nih.gov
| Model Category | : | Public |
| Date Published | : | July, 2019 |
| Healthcare Domain | : | Medical Technology |
| Code | : | github.com |
| Medical Imaging |
| Insight Extraction |