An Efficient Convolutional Neural Network to Detect and Count Blood Cells

 

Guardado en:
Detalles Bibliográficos
Autores: Joshi, Rakesh Chandra, Yadav, Saumya, Dutta, Malay Kishore, Travieso-González, Carlos M.
Formato: artículo original
Estado:Versión publicada
Fecha de Publicación:2022
Descripción:Blood cell analysis is an important part of the health and immunity assessment. There are three major components of the blood: red blood cells, white blood cells, and platelets. The count and density of these blood cells are used to find multiple disorders like blood infections (anemia, leukemia, among others). Traditional methods are time-consuming, and the test cost is high. Thus, it arises the need for automated methods that can detect different kinds of blood cells and count the number of cells. A convolutional neural network-based framework is proposed for detecting and counting the cells. The neural network is trained for the multiple iterations, and a model having lower validation loss is saved. The experiments are done to analyze the performance of the detection system and results with high accuracy in the counting of the cells. The mean average precision is achieved when compared to ground truth provided to respective labels. The value of the average precision is found to be ranging from 70% to 99.1%, with a mean average precision value of 85.35%. The proposed framework had much less time complexity: it took only 0.111 seconds to process an image frame with dimensions of 640×480 pixels. The system can also be implemented in low-cost, single-board computers for rapid prototyping. The efficiency of the proposed framework to identify and count different blood cells can be utilized to assist medical professionals in finding disorders and making decisions based on the obtained report.
País:Portal de Revistas UNA
Institución:Universidad Nacional de Costa Rica
Repositorio:Portal de Revistas UNA
Lenguaje:Inglés
OAI Identifier:oai:ojs.www.una.ac.cr:article/16303
Acceso en línea:https://www.revistas.una.ac.cr/index.php/uniciencia/article/view/16303
Access Level:acceso abierto
Palabra clave:convolutional neural network
deep learning
platelets
red blood cells
white blood cells
red neuronal convolucional
aprendizaje profundo
glóbulos rojos
glóbulos blancos