An Accurate Deep Learning-Based Computer-Aided Diagnosis System for Gastrointestinal Disease Detection Using Wireless Capsule Endoscopy Image Analysis

Peptic ulcers and stomach cancer are common conditions that impact the gastrointestinal (GI) system. Wireless capsule endoscopy (WCE) has emerged as a widely used, noninvasive technique for diagnosing these issues, providing valuable insights through the detailed imaging of the GI tract. Therefore, an early and accurate diagnosis of GI diseases is crucial for effective treatment. This paper introduces the Intelligent Learning Rate Controller (ILRC) mechanism that optimizes the training of deep learning (DL) models by adaptively adjusting the learning rate (LR) based on training progress. This helps improve convergence speed and reduce the risk of overfitting. The ILRC was applied to four DL models: EfficientNet-B0, ResNet101v2, InceptionV3, and InceptionResNetV2. These models were further enhanced using transfer learning, freezing layers, fine-tuning techniques, residual learning, and modern regularization methods. The models were evaluated on two datasets, the Kvasir-Capsule and KVASIR v2 datasets, which contain WCE images. The results demonstrated that the models, particularly when using ILRC, outperformed existing state-of-the-art methods in accuracy. On the Kvasir-Capsule dataset, the models achieved accuracies of up to 99.906%, and on the Kvasir-v2 dataset, they achieved up to 98.062%. This combination of techniques offers a robust solution for automating the detection of GI abnormalities in WCE images, significantly enhancing diagnostic efficiency and accuracy in clinical settings.