Deep Learning Algorithms for Detection and Classification of Gastrointestinal Diseases
Citations Over TimeTop 10% of 2021 papers
Abstract
Currently, nearly two million patients die of gastrointestinal diseases worldwide. Video endoscopy is one of the latest technologies in the medical imaging field for the diagnosis of gastrointestinal diseases, such as stomach ulcers, bleeding, and polyps. Medical video endoscopy generates many images, so doctors need considerable time to follow up all the images. This creates a challenge for manual diagnosis and has encouraged investigations into computer‐aided techniques to diagnose all the generated images in a short period and with high accuracy. The novelty of the proposed methodology lies in developing a system for diagnosis of gastrointestinal diseases. This paper introduces three networks, GoogleNet, ResNet‐50, and AlexNet, which are based on deep learning and evaluates them for their potential in diagnosing a dataset of lower gastrointestinal diseases. All images are enhanced, and the noise is removed before they are inputted into the deep learning networks. The Kvasir dataset contains 5,000 images divided equally into five types of lower gastrointestinal diseases (dyed‐lifted polyps, normal cecum, normal pylorus, polyps, and ulcerative colitis). In the classification stage, pretrained convolutional neural network (CNN) models are tuned by transferring learning to perform new tasks. The softmax activation function receives the deep feature vector and classifies the input images into five classes. All CNN models achieved superior results. AlexNet achieved an accuracy of 97%, sensitivity of 96.8%, specificity of 99.20%, and AUC of 99.98%.
Related Papers
- → Overview of deep learning in medical imaging(2017)1,052 cited
- → Deep learning ensemble 2D CNN approach towards the detection of lung cancer(2023)173 cited
- → Detecting Defects in PCB using Deep Learning via Convolution Neural Networks(2018)32 cited
- → Leaf Features Extraction for Plant Classification using CNN(2021)7 cited
- → Deep Convolution Neural Network for RBC Images(2022)2 cited