TY - GEN
T1 - Deep learning for blood cells classification based on multispectral imaging for improved accuracy
AU - Aung, Thiha
AU - Brady, James
AU - Hourani, Tetiana
AU - Elbourne, Aaron
AU - Walia, Sumeet
AU - Al-Hourani, Akram
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Cell classification is critical in biomedical applications, where recent developments in Multi-spectral imaging (MSI) techniques can capture detailed information about different biological cell types. For such applications, convolutional neural networks (CNNs) are typically used based on monochromatic or RGB microscope images. Increasing the spectral channels has been shown to provide higher classification accuracy due to the increased features exposed by the additional wavelengths. In this paper, we employ 3D image tensors with both spatial and spectral information by employing 3D-CNNs that divide the 3D image data into small cubes, capturing distinguishing features in both spatial and spectral dimensions. In order to quantify the improvement caused by increasing the chromatic channels, we explore the use of MSI and CNNs for frog blood cells classification. In this study, we employ up to six distinct optical channels to gain an understanding of classification improvement. Furthermore, we evaluate different wavelength combinations, measuring the impact on classification accuracy. Results demonstrate the significant performance enhancement of CNNs with MSI, emphasizing the importance of selecting the right wavelength combination. Our cost-effective approach has the potential for cell classification in medical applications, benefiting rapid disease diagnosis and treatment.
AB - Cell classification is critical in biomedical applications, where recent developments in Multi-spectral imaging (MSI) techniques can capture detailed information about different biological cell types. For such applications, convolutional neural networks (CNNs) are typically used based on monochromatic or RGB microscope images. Increasing the spectral channels has been shown to provide higher classification accuracy due to the increased features exposed by the additional wavelengths. In this paper, we employ 3D image tensors with both spatial and spectral information by employing 3D-CNNs that divide the 3D image data into small cubes, capturing distinguishing features in both spatial and spectral dimensions. In order to quantify the improvement caused by increasing the chromatic channels, we explore the use of MSI and CNNs for frog blood cells classification. In this study, we employ up to six distinct optical channels to gain an understanding of classification improvement. Furthermore, we evaluate different wavelength combinations, measuring the impact on classification accuracy. Results demonstrate the significant performance enhancement of CNNs with MSI, emphasizing the importance of selecting the right wavelength combination. Our cost-effective approach has the potential for cell classification in medical applications, benefiting rapid disease diagnosis and treatment.
KW - Cells classification
KW - Convolutional Neural Network
KW - Multispectral imaging
UR - http://www.scopus.com/inward/record.url?scp=85205973032&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85205973032&partnerID=8YFLogxK
U2 - 10.1109/AIoT63253.2024.00036
DO - 10.1109/AIoT63253.2024.00036
M3 - Conference paper
AN - SCOPUS:85205973032
SN - 9798350392302
T3 - Proceedings - 2024 IEEE Annual Congress on Artificial Intelligence of Things, AIoT 2024
SP - 142
EP - 147
BT - Proceedings - 2024 IEEE Annual Congress on Artificial Intelligence of Things, AIoT 2024
PB - IEEE, Institute of Electrical and Electronics Engineers
T2 - 2024 IEEE Annual Congress on Artificial Intelligence of Things, AIoT 2024
Y2 - 24 July 2024 through 26 July 2024
ER -