A deep convolutional neural network for bleeding detection in Wireless Capsule Endoscopy images

Abstract

Wireless Capsule Endoscopy (WCE) is a standard non-invasive modality for small bowel examination. Recently, the development of computer-aided diagnosis (CAD) systems for gastrointestinal (GI) bleeding detection in WCE image videos has become an active research area with the goal of relieving the workload of physicians. Existing methods based primarily on handcrafted features usually give insufficient accuracy for bleeding detection, due to their limited capability of feature representation. In this paper, we present a new automatic bleeding detection strategy based on a deep convolutional neural network and evaluate our method on an expanded dataset of 10,000 WCE images. Experimental results with an increase of around 2 percentage points in the Fi score demonstrate that our method outperforms the state-of-the-art approaches in WCE bleeding detection. The achieved Fi score is of up to 0.9955.

Deep Learning for Medical Image Processing: Overview, Challenges and the Future

“…Haloi [7] implemented five layers CNN with drop out mechanism for detec-tion of early stage DR on Retinopathy Online Challenge (ROC) and Massidor datasets and claimed t Sensitivity, Specificity, accuracy and area under the curve (AUC) up to 97%, 96%, 96% and 0.988 on Maddissor dataset and AUC up to 0.98 on ROC dataset. Alban [8] de-noised the angiograph images of EyePACS and then applied CNNs for detection of DR.…”

“…The food digestion and absorption is affected due to different ailments and diseases like inflammation, bleeding, infections and cancer in the GI tract [23]. [7]. The WCE is a non-invasive image video method for examination small bowel disease.…”

A Survey on WCE Abnormality Detection Methods

“…Specifically, studies carried out on influence of the number of visual words, the patch size and different classification methods in terms of classification performance. Xiao Jia et al [13] propose a new method for GI bleeding detection that can automatically and hierarchically learn high-level features via a deep neural network. The CNN has recently drawn great attention to the topic of "deep learning" within the computer vision field and been proved to have remarkable advancement not only in classification tasks of natural images but also in biomedical applications, such as cervical cell segmentation and mitosis detection.…”

A pilot trial of Convolution Neural Network for automatic retention-monitoring of capsule endoscopes in the stomach and duodenal bulb

“…How to predict the residence time of the CE in the stomach or duodenal bulb has not been solved, and medical staff may have to wait for several hours in the examination room to monitor whether the CE enters the descending segment of the duodenum (DSD) 5,6 . If the CE cannot enter the DSD in 2-3 h, some interventions, e.g., drugs or gastroscopy, can be used to push the CE forward into the DSD 7 , which is a tedious and boring task, especially for some patients who have to undergo the CE examination at the same time, which could greatly increase the monitoring workload for the medical staff.Artificial intelligence (AI), as a new technique, has been developed in the recent years, which includes Autoencoder 8 , Deep Belief Network 9 , Convolution Neural Network (CNN) 10 , and Deep Residual Network 11 , and they have been used in the medical image analysis and have been proved to be effective in some medical diagnostic fields, such as pulmonary nodules 12 , breast lesions 13,14 , skin cancer 15 , early gastrointestinal cancers 16,17 , polyps 18 , and small-bowel diseases [19][20][21][22][23] .Of those techniques, the CNN 24 is a type of deep learning mode 25-27 that requires the preprocessing of the image data inputted as a training image set for extracting specific features and quantities by using the multiple network layers (convolutional layers, fully-connected layers), and then iteratively changed through the multiple convolutions and the non-linear operations until the training data set is converted into a probability distribution of the potential image categories. With its high efficiency in the image analysis, the CNN has become a principal method of deep learning for images.…”

“…Artificial intelligence (AI), as a new technique, has been developed in the recent years, which includes Autoencoder 8 , Deep Belief Network 9 , Convolution Neural Network (CNN) 10 , and Deep Residual Network 11 , and they have been used in the medical image analysis and have been proved to be effective in some medical diagnostic fields, such as pulmonary nodules 12 , breast lesions 13,14 , skin cancer 15 , early gastrointestinal cancers 16,17 , polyps 18 , and small-bowel diseases [19][20][21][22][23] .…”