GFD Faster R-CNN: Gabor Fractal DenseNet Faster R-CNN for automatic detection of esophageal abnormalities in endoscopic images

Ghatwary, Noha, Zolgharni, Massoud and Ye, Xujiong (2019) GFD Faster R-CNN: Gabor Fractal DenseNet Faster R-CNN for automatic detection of esophageal abnormalities in endoscopic images. In: 10th International Workshop on Machine Learning in Medical Imaging, 13/10/2019, Shenzhen, China.

Full content URL: https://doi.org/10.1007/978-3-030-32692-0_11

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GFD Faster R-CNN: Gabor Fractal DenseNet Faster R-CNN for automatic detection of esophageal abnormalities in endoscopic images

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Abstract

Esophageal cancer is ranked as the sixth most fatal cancer type. Most esophageal cancers are believed to arise from overlooked abnormalities in the esophagus tube. The early detection of these abnormalities is considered challenging due to their different appearance and random location throughout the esophagus tube. In this paper, a novel Gabor Fractal DenseNet Faster R-CNN (GFD Faster R-CNN) is proposed which is a two-input network adapted from the Faster R-CNN to address the challenges of esophageal abnormality detection. First, a Gabor Fractal (GF) image is generated using various Gabor filter responses considering different orientations and scales, obtained from the original endoscopic image that strengthens the fractal texture information within the image. Secondly, we incorporate Densely Connected Convolutional Network (DenseNet) as the backbone network to extract features from both original endoscopic image and the generated GF image separately; the DenseNet provides a reduction in the trained parameters while supporting the network accuracy and enables a maximum flow of information. Features extracted from the GF and endoscopic images are fused through bilinear fusion before ROI pooling stage in Faster R-CNN, providing a rich feature representation that boosts the performance of final detection. The proposed architecture was trained and tested on two different datasets independently: Kvasir (1000 images) and MICCAI’15 (100 images). Extensive experiments have been carried out to evaluate the performance of the model, with a recall of 0.927 and precision of 0.942 for Kvasir dataset, and a recall of 0.97 and precision of 0.92 for MICCAI’15 dataset, demonstrating a high detection performance compared to the state-of-the-art.

Keywords:Gabor Fractal, DenseNet, Faster R-CNN, Esophagitis, EAC
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
ID Code:38162
Deposited On:30 Oct 2019 13:39

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