TY - GEN
T1 - RDNet
T2 - 9th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2022
AU - Zhu, Ziquan
AU - Ren, Zeyu
AU - Wang, Shui Hua
AU - Górriz, Juan M.
AU - Zhang, Yu Dong
N1 - Publisher Copyright:
© 2022, Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - (Aims) Blood cells are hematopoietic pluripotent stem cells derived from bone marrow. Blood diseases occur primarily in the hematopoietic system and can affect the hematopoietic system with abnormal blood changes, characterized by anemia, bleeding, and fever. It is helpful for doctors to diagnose blood diseases by classifying blood cells. However, doctors take a lot of time and energy to classify blood cells. The classification process is easily disturbed by external factors, such as doctors’ lack of rest, fatigue, etc. Many researchers use CNN to classify and detect red blood cells or white blood cells. However, using CNN has some problems in the classification or detection process. First, most researchers only classify blood cells into two categories, but there are many different types of blood cells. In addition, some studies are multi-classification of cells, but the results are often not ideal. (Methods) We propose a new model (RDNet) for the automatic classification of four types of blood cells to deal with these problems. The proposed RDNet selects the pre-trained ResNet-18 as the backbone. We transfer the pre-trained ResNet-18 because of the difference between the blood cell data set with the ImageNet data set. We add dropout to improve the classification performance. (Results) The accuracy of the proposed RDNet is 86.53%. The proposed RDNet obtains better accuracy than the transferred ResNet-18 because we add dropout in RDNet. Based on the accuracy, the proposed model is an effective tool to classify blood cells.
AB - (Aims) Blood cells are hematopoietic pluripotent stem cells derived from bone marrow. Blood diseases occur primarily in the hematopoietic system and can affect the hematopoietic system with abnormal blood changes, characterized by anemia, bleeding, and fever. It is helpful for doctors to diagnose blood diseases by classifying blood cells. However, doctors take a lot of time and energy to classify blood cells. The classification process is easily disturbed by external factors, such as doctors’ lack of rest, fatigue, etc. Many researchers use CNN to classify and detect red blood cells or white blood cells. However, using CNN has some problems in the classification or detection process. First, most researchers only classify blood cells into two categories, but there are many different types of blood cells. In addition, some studies are multi-classification of cells, but the results are often not ideal. (Methods) We propose a new model (RDNet) for the automatic classification of four types of blood cells to deal with these problems. The proposed RDNet selects the pre-trained ResNet-18 as the backbone. We transfer the pre-trained ResNet-18 because of the difference between the blood cell data set with the ImageNet data set. We add dropout to improve the classification performance. (Results) The accuracy of the proposed RDNet is 86.53%. The proposed RDNet obtains better accuracy than the transferred ResNet-18 because we add dropout in RDNet. Based on the accuracy, the proposed model is an effective tool to classify blood cells.
KW - Blood cells
KW - Convolutional neural network
KW - Dropout
KW - ResNet-18
KW - Transfer learning
UR - http://www.scopus.com/inward/record.url?scp=85132014870&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-06242-1_14
DO - 10.1007/978-3-031-06242-1_14
M3 - Conference Proceeding
AN - SCOPUS:85132014870
SN - 9783031062414
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 136
EP - 144
BT - Artificial Intelligence in Neuroscience
A2 - Ferrández Vicente, José Manuel
A2 - Álvarez-Sánchez, José Ramón
A2 - de la Paz López, Félix
A2 - Adeli, Hojjat
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 31 May 2022 through 3 June 2022
ER -