Attention Based Fused CNN for the Early Prediction of Gastrointestinal Disease

This study proposes a standardized framework for early prediction of gastrointestinal disease diseases leveraging advanced image pre-processing, data augmentation, and an attention-based fused Convolutional Neural Network (CNN). The framework initiates meticulous image pre-processing, encompassing cropping, standardization and resizing methodologies to standardize image inputs and accentuate pertinent features. Following pre-processing, data augmentation strategies are implemented to diversify the training dataset, enhancing model robustness and generalization. By artificially generating diverse variations of the original images, data augmentation helps expand the training dataset. Techniques such as rotation, translation, scaling, and flipping are applied to augment the dataset, enabling the CNN algorithm to generalize better and capture a broader range of disease patterns. The key component of the framework is an attention-based fused CNN architecture designed to capture spatial and channel-wise dependencies within gastrointestinal images effectively. The attention mechanism allows the network to concentrate on informative regions while suppressing irrelevant noise, enhancing feature representation and classification performance. The experimental outcomes proved that the developed framework attains superior performance in the early prediction of gastrointestinal diseases with a high accuracy of 98.9%. The combination of image pre-processing, data augmentation and attention-based fused CNN contributes to improved accuracy and robustness.