TY - GEN
T1 - An approach towards automatic detection of toxoplasmosis using fundus images
AU - Chakravarthy, Adithi Deborah
AU - Abeyrathna, Dilanga
AU - Subramaniam, Mahadevan
AU - Chundi, Parvathi
AU - Halim, Muhammad Sohail
AU - Hasanreisoglu, Murat
AU - Sepah, Yasir J.
AU - Nguyen, Quan Dong
N1 - Funding Information:
This work was conducted while Dr. Subramaniam was a visiting scholar at the Byers Eye Institute, Stanford University, who thanks the Stanford School of Medicine for supporting his stay. Dr. Chundi and Dr. Subramaniam also thank the Nebraska Research Initiative Award for its support of Chakravarthy and Abeyrathna
Publisher Copyright:
© 2019 IEEE.
PY - 2019/10
Y1 - 2019/10
N2 - Ocular Toxoplasmosis (OT) is a widespread infectious chorioretinal disease whose timely diagnosis and treatment are crucial to prevent potential vision loss. Diagnosing OT is a challenging task ranging from tedious analyses of fundus images of the eye to serological clinical tests. An automated approach using convolutional neural networks (CNNs) towards diagnosing OT by analyzing fundus images is described. Fundus images are segmented to patches using a sliding window and are classified into healthy and unhealthy fundus image patches using a CNN model. An OT lesion heat map of a fundus image is generated from these patches. The heat map and patch features are then combined to develop a dual input hybrid CNN model detecting OT fundus images with high accuracy. The approach was applied to a dataset of fundus images involving OT and normal subjects and was highly effective in identifying fundus images having OT lesions.
AB - Ocular Toxoplasmosis (OT) is a widespread infectious chorioretinal disease whose timely diagnosis and treatment are crucial to prevent potential vision loss. Diagnosing OT is a challenging task ranging from tedious analyses of fundus images of the eye to serological clinical tests. An automated approach using convolutional neural networks (CNNs) towards diagnosing OT by analyzing fundus images is described. Fundus images are segmented to patches using a sliding window and are classified into healthy and unhealthy fundus image patches using a CNN model. An OT lesion heat map of a fundus image is generated from these patches. The heat map and patch features are then combined to develop a dual input hybrid CNN model detecting OT fundus images with high accuracy. The approach was applied to a dataset of fundus images involving OT and normal subjects and was highly effective in identifying fundus images having OT lesions.
KW - Deep Learning
KW - Medical Imaging
KW - Neural Networks
KW - Ocular Toxoplasmosis
UR - http://www.scopus.com/inward/record.url?scp=85078030948&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85078030948&partnerID=8YFLogxK
U2 - 10.1109/BIBE.2019.00134
DO - 10.1109/BIBE.2019.00134
M3 - Conference contribution
AN - SCOPUS:85078030948
T3 - Proceedings - 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering, BIBE 2019
SP - 710
EP - 717
BT - Proceedings - 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering, BIBE 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th International Conference on Bioinformatics and Bioengineering, BIBE 2019
Y2 - 28 October 2019 through 30 October 2019
ER -