Esophageal abnormality detection using DenseNet based Faster R-CNN with Gabor features

Ghatwary, Noha, Ye, Xujiong and Zolgharni, Massoud ORCID: (2019) Esophageal abnormality detection using DenseNet based Faster R-CNN with Gabor features. IEEE Access, 7. p. 1.

[thumbnail of Zolgharni_IEEE_Access_2019_Esophageal_abnormality_detection_using_DenseNet_based_Faster_R-CNN_with_Gabor_features.pdf]
Zolgharni_IEEE_Access_2019_Esophageal_abnormality_detection_using_DenseNet_based_Faster_R-CNN_with_Gabor_features.pdf - Accepted Version
Available under License Creative Commons Attribution.

Download (17MB) | Preview


Early detection of esophageal abnormalities can help in preventing the progression of the disease into later stages. During esophagus examination, abnormalities are often overlooked due to the irregular shape, variable size and the complex surrounding area which requires a significant effort and experience. In this paper, a novel deep learning model which is based on Faster Region-Based Convolution Neural Network (Faster R-CNN) is presented to automatically detect abnormalities in the esophagus from endoscopic images. The proposed detection system is based on a combination of Gabor handcrafted features with CNN features. The Densely Connected Convolution Networks (DenseNets) architecture is embraced to extract CNN features providing a strengthened feature propagation between the layers and allay the vanishing gradient problem. To address the challenges of detecting abnormal complex regions, we propose fusing extracted Gabor features with CNN features through concatenation to enhance texture details in the detection stage. Our newly designed architecture is validated on two datasets (Kvasir and MICCAI 2015). Regarding the Kvasir, the results show an outstanding performance with a recall of 90.2% and precision of 92.1% with a mean of average precision (mAP) of 75.9%. While for the Miccai 2015 dataset, the model is able to surpass the state-of-the-art performance with 95% recall and 91% precision with mAP value of 84%. Experimental results demonstrate that the system is able to detect abnormalities in endoscopic images with good performance without any human intervention.

Item Type: Article
Identifier: 10.1109/ACCESS.2019.2925585
Additional Information: This work is licensed under a Creative Commons Attribution 3.0 License.
Keywords: Detection, DenseNet, EAC, Esophagitis, Faster R-CNN, HD-WLE.
Subjects: Computing > Intelligent systems
Depositing User: Massoud Zolgharni
Date Deposited: 28 Jun 2019 10:18
Last Modified: 06 Feb 2024 16:00


Downloads per month over past year

Actions (login required)

View Item View Item