A deep learning image classification method and its application introducing hybrid attention mechanism

We introduce a deep learning methodology that combines the Inception-ResNet-V2 architecture with a hybrid attention mechanism. This fusion enables the extraction of crucial information from both channel and spatial dimensions within feature maps, giving rise to the ECA-SA-Inception-ResNet model. To assess the performance of our model, we conducted experiments on a petrographic thin section image dataset utilizing Grad-CAM visualization techniques. The results demonstrate the model's proficiency in extracting essential features from petrographic thin section images, thereby resulting in substantial enhancements in image classification accuracy.