Abstract

Wireless capsule endoscopy is a noninvasive wireless imaging technology that becomes increasingly popular in recent years. One of the major drawbacks of this technology is that it generates a large number of photos that must be analyzed by medical personnel, which takes time. Various research groups have proposed different image processing and machine learning techniques to classify gastrointestinal tract diseases in recent years. Traditional image processing algorithms and a data augmentation technique are combined with an adjusted pretrained deep convolutional neural network to classify diseases in the gastrointestinal tract from wireless endoscopy images in this research. We take advantage of pretrained models VGG16, ResNet-18, and GoogLeNet, a convolutional neural network (CNN) model with adjusted fully connected and output layers. The proposed models are validated with a dataset consisting of 6702 images of 8 classes. The VGG16 model achieved the highest results with 96.33% accuracy, 96.37% recall, 96.5% precision, and 96.5% F1-measure. Compared to other state-of-the-art models, the VGG16 model has the highest Matthews Correlation Coefficient value of 0.95 and Cohen's kappa score of 0.96.

Highlights

  • Esophageal, stomach, and colorectal cancers account for 2.8 million new cases and 1.8 million deaths worldwide per year

  • The doctor employs the wireless capsule endoscopy (WCE) procedure to inspect the interior of the gastrointestinal tract (GIT)

  • From the Matthews Correlation Coefficient (MCC) of all the states of the method, we found that the modified VGG16 method proves to be a perfect agreement for classifying GIT diseases

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Summary

Introduction

Esophageal, stomach, and colorectal cancers account for 2.8 million new cases and 1.8 million deaths worldwide per year. Out of these ulcers, bleeding and polyps are all examples of gastrointestinal infections [1]. The doctor employs the WCE procedure to inspect the interior of the gastrointestinal tract (GIT). The doctor uses the WCE method to inspect the interior of the gastrointestinal tract in order to discover disease (GIT) [5, 6]. The video frames received are examined by the Computational and Mathematical Methods in Medicine physician to decide about the diseases [7]. The major diseases diagnosed using the WCE are ulcers, bleeding, malignancy, and polyps in the digestive system. Owing to inexperience or negligence, it may often result in a misdiagnosis [10]

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