eprintid: 11827 rev_number: 2 eprint_status: archive userid: 1 dir: disk0/00/01/18/27 datestamp: 2023-11-10 03:26:21 lastmod: 2023-11-10 03:26:21 status_changed: 2023-11-10 01:16:14 type: conference_item metadata_visibility: show creators_name: Kamble, R.M. creators_name: Kokare, M. creators_name: Chan, G.C.Y. creators_name: Perdomo, O. creators_name: González, F.A. creators_name: Müller, H. creators_name: Mériaudeau, F. title: Automated Diabetic Macular Edema (DME) analysis using fine tuning with inception-resnet-v2 on oct images ispublished: pub keywords: Biomedical engineering; Convolution; Medicine; Neural networks; Statistical methods; Tomography, Chinese universities; Classification accuracy; Convolutional neural network; Developed model; Fine tuning; Leave-one-out cross validations; Macular edema; Research institutes, Optical tomography note: cited By 35; Conference of 2018 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2018 ; Conference Date: 3 December 2018 Through 6 December 2018; Conference Code:144644 abstract: Accurate detection of diabetic macular edema (DME) is an important task in optical coherence tomography (OCT) images of the eye. A relatively simple and practical approach is proposed in this paper. A pre-trained convolutional neural network (CNN) is fine tuned for a classification of DME versus normal cases. The fine-tuned Inception-Resnet-v2 CNN model can effectively identify pathologies in comparison to classical learning. Experiments were carried out on the publicly available data set of the Singapore Eye Research Institute (SERI). The developed model was also compared to other fine tuned models, such as Resnet-50 and Inception-v3. The proposed method achieved 100 classification accuracy with the Inception-Resnet-v2 model using a leave-one-out cross-validation strategy. For robustness, the model trained on the SERI dataset was tested on another dataset provided by the Chinese University HongKong (CUHK), also with 100 accuracy. The proposed method is a potentially impactful tool for accurately detecting DME vs. normal cases. © 2018 IEEE date: 2019 publisher: Institute of Electrical and Electronics Engineers Inc. official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85062772479&doi=10.1109%2fIECBES.2018.8626616&partnerID=40&md5=5afcb4ca34c7fd4c0676b9c468644f97 id_number: 10.1109/IECBES.2018.8626616 full_text_status: none publication: 2018 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2018 - Proceedings pagerange: 442-446 refereed: TRUE isbn: 9781538624715 citation: Kamble, R.M. and Kokare, M. and Chan, G.C.Y. and Perdomo, O. and González, F.A. and Müller, H. and Mériaudeau, F. (2019) Automated Diabetic Macular Edema (DME) analysis using fine tuning with inception-resnet-v2 on oct images. In: UNSPECIFIED.