Zahedi Nasab, R.; Mohseni, H.; Montazeri, M.; Ghasemian, F.; Amin, S. AFEX-Net: Adaptive Feature EXtraction CNN for Classifying CT Images. Preprints2023, 2023060755. https://doi.org/10.20944/preprints202306.0755.v1
APA Style
Zahedi Nasab, R., Mohseni, H., Montazeri, M., Ghasemian, F., & Amin, S. (2023). AFEX-Net: Adaptive Feature EXtraction CNN for Classifying CT Images. Preprints. https://doi.org/10.20944/preprints202306.0755.v1
Chicago/Turabian Style
Zahedi Nasab, R., Fahimeh Ghasemian and Sobhan Amin. 2023 "AFEX-Net: Adaptive Feature EXtraction CNN for Classifying CT Images" Preprints. https://doi.org/10.20944/preprints202306.0755.v1
Abstract
Deep convolutional neural networks (CNN) are favored methods widely used in medical
image processing due to their assured shown performance. Recently, the emergence
of new lung diseases and the possibility of early detection of their symptoms has
attracted many researchers to classify diseases by training deep CNNs on lung CT
images. The trained networks are expected to distinguish between lung indications in
different diseases, especially at the early stages of them. With the hope of achieving this
purpose, we proposed an efficient deep CNN called AFEX-Net with adaptive feature
extraction layers that successfully extract distinguishing features and classify chest CT
images. The efficiency of the proposed network has two aspects: it is a lightweight
network with low number of parameters and fast training and it has adaptive pooling
layers and adaptive activation functions to increase its level of compatibility to the
input data. The proposed network has been evaluated on a dataset with more than
10K chest CT slices, while an efficient pre-processing method is developed to remove
any bias from the images. Additionally, we evaluated the performance of the proposed
model on the public COVID-CTset dataset to prove the generalisability of our model.
The obtained results confirm the competence of the proposed network in confronting
medical images, where prompt and accurate learning is required.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
