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Deep learning and computer vision that become popular in recent years are advantage techniques in medical diagnosis. A large database of Optical Coherence Tomography (OCT) images can be used to train a deep learning model which can support and suggest effectively illnesses and status of a patient. Therefore, semantic image segmentation is used to detect and categorize anomaly regions in OCT images. However, numerous existing approaches ignored spatial structure as well as contextual information in a given image. To overcome existing problems, this work proposes a novel method which takes advantage of the deep convolutional neural network, attention block, pyramid pooling module and auxiliary connections between layers. Attention block helps to detect the spatial structure of a given image. Beside, pyramid pooling module has a responsibility to identify the shape and margin of the anomaly region. In additional, auxiliary connections support to enrich useful information pass through one layer as well as reduce overfitting problem. Our work produces higher accuracy than state-of-the-art methods with 78.19% comparing to Deeplab v3 76.19% and Bisenet 76.85% in term of dice coefficient. Additionally, a number of parameters in our work is smaller than the previous approaches.
|Title of host publication||2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||4|
|State||Published - Nov 2019|
|Event||2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019 - Lanzhou, China|
Duration: 18 Nov 2019 → 21 Nov 2019
|Name||2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019|
|Conference||2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA ASC 2019|
|Period||18/11/19 → 21/11/19|
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