Iqbal, S.; Naqvi, S.S.; Khan, H.A.; Saadat, A.; Khan, T.M. G-Net Light: A Lightweight Modified Google Net for Retinal Vessel Segmentation. Photonics2022, 9, 923.
Iqbal, S.; Naqvi, S.S.; Khan, H.A.; Saadat, A.; Khan, T.M. G-Net Light: A Lightweight Modified Google Net for Retinal Vessel Segmentation. Photonics 2022, 9, 923.
Iqbal, S.; Naqvi, S.S.; Khan, H.A.; Saadat, A.; Khan, T.M. G-Net Light: A Lightweight Modified Google Net for Retinal Vessel Segmentation. Photonics2022, 9, 923.
Iqbal, S.; Naqvi, S.S.; Khan, H.A.; Saadat, A.; Khan, T.M. G-Net Light: A Lightweight Modified Google Net for Retinal Vessel Segmentation. Photonics 2022, 9, 923.
Abstract
Convolutional neural network architectures have become increasingly complex, which has improved the performance slowly on well-known benchmark datasets in the recent years. In this research, we have analyzed the true need for such complexity. We have introduced G-Net light, a lightweight modified GoogleNet with improved filter count per layer to reduce feature overlaps and complexity. Additionally, by limiting the amount of pooling layers in the proposed architecture, we have exploited the skip connections to minimize the spatial information loss. The investigations on the proposed architecture are evaluated on three retinal vessel segmentation publicly available datasets. The proposed G-Net light outperforms other vessel segmentation architectures by reducing the number of trainable parameters..
Keywords
Deep Learning; Convolutional Neural Networks; Medical Image Segmentation
Subject
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.
