NDLI: SODNet: small object detection using deconvolutional neural network

Content Provider	IET Digital Library
Author	Zhang, Xinpeng Wu, Jigang Peng, Zhihao Meng, Min
Abstract	Convolution neural network (CNN) is an efficient technique to detect objects in various kinds of images, especially for microaneurysm (MA) of diabetic retinopathy in retinal fundus image. This study proposes a deconvolutional neural network to accurately discriminate MA from non-MA. The deconvolution, instead of pooling operation, is embedded into the CNN to recover the erased details of feature maps of convolutional layers. Three types of images are collected for training and predicting. Furthermore, the extracted features are fed into the fully-connected layers to classify using a softmax layer. Experimental results demonstrate that the proposed method can achieve significant sensitivity and accuracy on multiple public datasets, in comparison to the state-of-the-art. For Retinopathy Online Challenge dataset, the sensitivity and accuracy are improved up to 0.798 and 0.986, respectively.
Starting Page	1662
Ending Page	1669
Page Count	8
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 8, Jun (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/8
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.0833
Journal	IET Image Processing
Publisher Date	2020-04-02
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Biology And Medical Computing Biomedical Imaging/measurement Biomedical Optical Imaging CNN Computer Vision And Image Processing Technique Convolution Neural Network Convolutional Layer Convolutional Neural Nets Deconvolution Layer Deconvolutional Neural Network Diabetic Retinopathy Diseases Eye Feature Extraction Image Classification Learning in AI Medical Image Processing Microaneurysm Neural Computing Technique NonMA Object Detection Optical And Laser Radiation Optical, Image And Video Signal Processing Patient Diagnostic Method And Instrumentation Retinal Fundus Image Retinopathy Online Challenge Dataset Softmax Layer
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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