Automated Diabetic Macular Edema (DME) analysis using fine tuning with inception-resnet-v2 on oct images

Ravi M. Kamble, Manesh Kokare, Genevieve C.Y. Chan, Oscar Perdomo, Fabio A. González, Henning Müller, Fabrice Mériaudeau

Resultado de la investigación: Capítulo en Libro/Reporte/ConferenciaContribución a la conferencia

10 Citas (Scopus)

Resumen

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.

Idioma originalInglés estadounidense
Título de la publicación alojada2018 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2018 - Proceedings
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas442-446
Número de páginas5
ISBN (versión digital)9781538624715
DOI
EstadoPublicada - ene 24 2019
Publicado de forma externa
Evento2018 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2018 - Kuching, Malasia
Duración: dic 3 2018dic 6 2018

Serie de la publicación

Nombre2018 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2018 - Proceedings

Conferencia

Conferencia2018 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2018
País/TerritorioMalasia
CiudadKuching
Período12/3/1812/6/18

All Science Journal Classification (ASJC) codes

  • Ingeniería biomédica
  • Medicina (miscelánea)
  • Informática aplicada a la salud
  • Instrumental

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