TY - GEN
T1 - AlexNet for Image-Based COVID-19 Diagnosis
AU - Tang, Min
AU - Peng, Yibin
AU - Wang, Shuihua
AU - Chen, Shuwen
AU - Zhang, Yudong
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024
Y1 - 2024
N2 - The medical community is working harder to develop quick and accurate methods of diagnosing the virus in response to the COVID-19 pandemic. The speed and efficiency of image-based COVID-19 diagnosis are its benefits, but it also carries a risk of error and necessitates the involvement of numerous skilled radiologists. In this paper, a novel convolutional neural network architecture called AlexNet is presented. It has the capacity to automatically learn features in a hierarchy and recognize complex patterns and improves the model’s recognition of disease-related features. In addition, AlexNet’s adaptability and generalization capabilities contribute to its effectiveness in processing various imaging datasets. AlexNet therefore has great potential to identify complex patterns associated with COVID-19-related lung abnormalities. Nevertheless, it also has certain limitations, including the need for a large amount of processing power, the possibility of overfitting, the lack of sufficient interpretability, and the need for further development in order to make it more applicable to particular diagnostic tasks. In summary, collaborative efforts between the AI research community and healthcare professionals will continue to seek accurate, efficient, and ethical solutions for image-based COVID-19 diagnosis.
AB - The medical community is working harder to develop quick and accurate methods of diagnosing the virus in response to the COVID-19 pandemic. The speed and efficiency of image-based COVID-19 diagnosis are its benefits, but it also carries a risk of error and necessitates the involvement of numerous skilled radiologists. In this paper, a novel convolutional neural network architecture called AlexNet is presented. It has the capacity to automatically learn features in a hierarchy and recognize complex patterns and improves the model’s recognition of disease-related features. In addition, AlexNet’s adaptability and generalization capabilities contribute to its effectiveness in processing various imaging datasets. AlexNet therefore has great potential to identify complex patterns associated with COVID-19-related lung abnormalities. Nevertheless, it also has certain limitations, including the need for a large amount of processing power, the possibility of overfitting, the lack of sufficient interpretability, and the need for further development in order to make it more applicable to particular diagnostic tasks. In summary, collaborative efforts between the AI research community and healthcare professionals will continue to seek accurate, efficient, and ethical solutions for image-based COVID-19 diagnosis.
KW - AlexNet
KW - COVID-19
KW - X-rays and CT scans
UR - http://www.scopus.com/inward/record.url?scp=85188669161&partnerID=8YFLogxK
U2 - 10.1007/978-981-97-1335-6_16
DO - 10.1007/978-981-97-1335-6_16
M3 - Conference Proceeding
AN - SCOPUS:85188669161
SN - 9789819713349
T3 - Lecture Notes in Electrical Engineering
SP - 166
EP - 176
BT - Proceedings of 2023 International Conference on Medical Imaging and Computer-Aided Diagnosis (MICAD 2023) - Medical Imaging and Computer-Aided Diagnosis
A2 - Su, Ruidan
A2 - Zhang, Yu-Dong
A2 - Frangi, Alejandro F.
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Conference on Medical Imaging and Computer-Aided Diagnosis, MICAD 2023
Y2 - 9 December 2023 through 10 December 2023
ER -