Abstract

Gastrointestinal (GI) diseases often represent the most frequent and common high-risk diseases. Wireless capsule endoscopy (WCE) has changed the landscape of diagnosing and treating patients. Endoscopists commonly utilize wireless capsule endoscopy to assess the majority of intestinal conditions, particularly with respect to polyps and ulcers. The use of WCE has shown a ten percent increase in Indian hospitals. Medical assessments are typically time-consuming and expensive, especially given the necessity to investigate directly from endoscopic videos. These confines are alleviated with the assistance of artificial intelligence and deep learning, which provide an efficient platform for instantaneous defect detection. The objective served by this examination is to assist endoscopic image classification work for clinical investigators. The paper proposed a deep-learning model named Endo-CNN based on convolutional neural network to classify endoscopic images according to the identified disease. The classes of images include polyps, ulcerative colitis, esophagitis and a healthy colon. Data augmentation occurs to reduce the imbalance of datasets and to evaluate the model performance that exceeds 48,000 images. The model achieves a positive accuracy rate with all the image classes. There are various aspects of an identified disease because of the variety of sizes, shapes and textures as well as colors. The paper also performs a comparative study of the designed model and against other pre-trained models (VGG16, ResNet50 & DenseNet). This paper can act as a baseline for many future solutions in the field of gastroenterology.

Keywords

CNN, Data augmentation, Deep learning, Endoscopic images, Gastroenterology, WCE,

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References

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