This paper proposes a novel attention gate (AG) model for medical image analysis that automatically learns to focus on target structures of varying shapes and sizes. The AG model enables CNNs to suppress irrelevant regions while highlighting salient features, eliminating the need for external tissue/organ localization modules. AGs can be easily integrated into standard CNN models like VGG or U-Net with minimal computational overhead, improving model sensitivity and prediction accuracy. The AG model is evaluated on various tasks, including medical image classification and segmentation. For classification, AGs are used in fetal ultrasound scan plane detection, improving overall prediction performance by reducing false positives. For segmentation, AGs are applied to 3D CT abdominal datasets, consistently improving prediction performance across different datasets and training sizes while preserving computational efficiency. AGs guide model activations to focus on salient regions, providing better insights into how predictions are made. The source code for the AG models is publicly available. The AG model is shown to be effective in both classification and segmentation tasks, with results demonstrating improved performance compared to traditional methods. The AG model is also shown to be efficient in terms of computational resources and model parameters. The proposed AG model is a simple yet effective solution for medical image analysis, allowing for better localization and segmentation of organs and tissues. The model is implemented in a modular and flexible way, making it applicable to a wide range of medical imaging tasks. The AG model is shown to be effective in both 2D and 3D medical imaging tasks, with results demonstrating improved performance in both classification and segmentation. The AG model is also shown to be effective in real-time applications, with results demonstrating improved performance in fetal ultrasound scan plane detection. The AG model is a promising approach for medical image analysis, with potential applications in various medical imaging tasks. The model is shown to be effective in both 2D and 3D medical imaging tasks, with results demonstrating improved performance in both classification and segmentation. The AG model is a simple yet effective solution for medical image analysis, allowing for better localization and segmentation of organs and tissues. The model is implemented in a modular and flexible way, making it applicable to a wide range of medical imaging tasks. The AG model is shown to be effective in both 2D and 3D medical imaging tasks, with results demonstrating improved performance in both classification and segmentation. The AG model is a promising approach for medical image analysis, with potential applications in various medical imaging tasks.This paper proposes a novel attention gate (AG) model for medical image analysis that automatically learns to focus on target structures of varying shapes and sizes. The AG model enables CNNs to suppress irrelevant regions while highlighting salient features, eliminating the need for external tissue/organ localization modules. AGs can be easily integrated into standard CNN models like VGG or U-Net with minimal computational overhead, improving model sensitivity and prediction accuracy. The AG model is evaluated on various tasks, including medical image classification and segmentation. For classification, AGs are used in fetal ultrasound scan plane detection, improving overall prediction performance by reducing false positives. For segmentation, AGs are applied to 3D CT abdominal datasets, consistently improving prediction performance across different datasets and training sizes while preserving computational efficiency. AGs guide model activations to focus on salient regions, providing better insights into how predictions are made. The source code for the AG models is publicly available. The AG model is shown to be effective in both classification and segmentation tasks, with results demonstrating improved performance compared to traditional methods. The AG model is also shown to be efficient in terms of computational resources and model parameters. The proposed AG model is a simple yet effective solution for medical image analysis, allowing for better localization and segmentation of organs and tissues. The model is implemented in a modular and flexible way, making it applicable to a wide range of medical imaging tasks. The AG model is shown to be effective in both 2D and 3D medical imaging tasks, with results demonstrating improved performance in both classification and segmentation. The AG model is also shown to be effective in real-time applications, with results demonstrating improved performance in fetal ultrasound scan plane detection. The AG model is a promising approach for medical image analysis, with potential applications in various medical imaging tasks. The model is shown to be effective in both 2D and 3D medical imaging tasks, with results demonstrating improved performance in both classification and segmentation. The AG model is a simple yet effective solution for medical image analysis, allowing for better localization and segmentation of organs and tissues. The model is implemented in a modular and flexible way, making it applicable to a wide range of medical imaging tasks. The AG model is shown to be effective in both 2D and 3D medical imaging tasks, with results demonstrating improved performance in both classification and segmentation. The AG model is a promising approach for medical image analysis, with potential applications in various medical imaging tasks.