TY - JOUR
T1 - Multiscale triplet spatial information fusion-based deep learning method to detect retinal pigment signs with fundus images
AU - Arsalan, Muhammad
AU - Haider, Adnan
AU - Park, Chanhum
AU - Hong, Jin Seong
AU - Park, Kang Ryoung
N1 - Publisher Copyright:
© 2024 The Authors
PY - 2024/7
Y1 - 2024/7
N2 - Inherited retinal diseases (IRDs) are genetic disorders that cause progressive deterioration of the photoreceptors associated with vision loss or blindness. Retinitis pigmentosa (RP) is a rare hereditary ophthalmic disease that initially causes night blindness owing to continuous retinal pigment deterioration. A computer-aided diagnosis (CAD)-based RP diagnosis solution by pigment sign detection can help ophthalmologists to analyze and treat the disease timely. At present, most of the research addresses retinal disease CAD using expensive optical coherence tomography (OCT); however, fundus imaging-based solutions are quick, convenient, and inexpensive for massive screening. This study proposes two convolutional neural networks (CNNs)-based segmentation that combines multiscale features by spatial information fusion: a single spatial fusion network (SSF-Net) and a triplet spatial fusion network (TSF-Net). SSF-Net fuses four multiscale spatial information streams. TSF-Net exploits triplet spatial information fusion by early, intermediate, and late fusion to ensure the fine segmentation of retinal pigment signs without preprocessing. TSF-Net creates a valuable difference in performance over SSF-Net. To evaluate SSF-Net and TSF-Net, the open dataset, named Retinal Images for Pigment Signs is utilized with 4-fold cross-validation. The experiment results confirm that SSF-Net and TSF-Net demonstrate superior performance compared to the state-of-the-art methods for the screening and analysis of RP disease.
AB - Inherited retinal diseases (IRDs) are genetic disorders that cause progressive deterioration of the photoreceptors associated with vision loss or blindness. Retinitis pigmentosa (RP) is a rare hereditary ophthalmic disease that initially causes night blindness owing to continuous retinal pigment deterioration. A computer-aided diagnosis (CAD)-based RP diagnosis solution by pigment sign detection can help ophthalmologists to analyze and treat the disease timely. At present, most of the research addresses retinal disease CAD using expensive optical coherence tomography (OCT); however, fundus imaging-based solutions are quick, convenient, and inexpensive for massive screening. This study proposes two convolutional neural networks (CNNs)-based segmentation that combines multiscale features by spatial information fusion: a single spatial fusion network (SSF-Net) and a triplet spatial fusion network (TSF-Net). SSF-Net fuses four multiscale spatial information streams. TSF-Net exploits triplet spatial information fusion by early, intermediate, and late fusion to ensure the fine segmentation of retinal pigment signs without preprocessing. TSF-Net creates a valuable difference in performance over SSF-Net. To evaluate SSF-Net and TSF-Net, the open dataset, named Retinal Images for Pigment Signs is utilized with 4-fold cross-validation. The experiment results confirm that SSF-Net and TSF-Net demonstrate superior performance compared to the state-of-the-art methods for the screening and analysis of RP disease.
KW - Computer-aided diagnosis
KW - Fundus images
KW - Single spatial fusion network
KW - Triplet spatial fusion network
KW - deep learning
UR - http://www.scopus.com/inward/record.url?scp=85189553157&partnerID=8YFLogxK
U2 - 10.1016/j.engappai.2024.108353
DO - 10.1016/j.engappai.2024.108353
M3 - Article
AN - SCOPUS:85189553157
SN - 0952-1976
VL - 133
JO - Engineering Applications of Artificial Intelligence
JF - Engineering Applications of Artificial Intelligence
M1 - 108353
ER -