Automated Classification of Gastrointestinal Abnormalities using Convolutional Neural Networks
Keywords:
Deep learning, Convolutional neural networks (CNNs), Capsule endoscopy, Pre-trained Modes, Gastrointestinal abnormalitiesAbstract
With the use of a tiny, ingestible capsule fitted with a camera, a cutting-edge medical imaging technology called capsule endoscopy, high-resolution images of the digestive system can be obtained. By allowing for non-invasive digestion system viewing, this technique has revolutionized the diagnosis and monitoring of gastrointestinal illnesses. This paper is focused on the creation of sophisticated picture classification algorithms to increase the clinical value of capsule endoscopy. Five distinct CNN models were used in the study: DenseNet121, EfficientNetB4, EfficientNetV2B3, ResNet101, and InceptionV3. Out of all the models, the DenseNet121 model performed the best, showing higher accuracy, AUC, precision, and recall. Its accuracy was 94.93%, recall was 93.87%, precision was 96.c9%, and AUC was 99.79%. =
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