Gastrointestinal image classification with GIDNet CNN model and non-linear Tansh activation function

Key Points

• GIDNet CNN model achieves high accuracy in gastrointestinal image classification, enabling better diagnostic capabilities.
• The implementation of non-linear Tansh activation function enhances model performance, with improved predictive accuracy observed.
• Deep learning techniques were applied for gastrointestinal image classification, leveraging advanced neural network architectures.
• Findings highlight the potential for improved diagnostic accuracy in gastrointestinal conditions through innovative AI applications.