… , the fusion of deep learning classifiers and medical images … In this paper, we have
proposed a deep transfer learning … In the obtained results, the proposed deep learning model …

… weighted Class Activation Mapping (Grad-CAM), uses the gradients … Unlike previous
approaches, GradCAM is applicable to a … answering) or reinforcement learning, without …

Background Deep learning using convolutional neural networks (CNNs) has shown great
promise in advancing neuroscience research. However, the ability to interpret the CNNs lags …

… more reliable than AlexNet, while with Guided Grad-CAM they score VGG as clearly more
reliable than AlexNet. Thus our Guided GradCAM visualization can help users place trust in a …

… 6 we show certain use cases of Grad-CAM such as diagnosing image classification CNNs
… textual explanations with Grad-CAM. In Sect. 8 we show how Grad-CAM can be applied to …

… the performance of two deep learning models for the classification of MI. After that, we applied
a class activation mapping (CAM) visualization technique called Grad-CAM to the model …

This paper proposes an automatic method for classifying Aortic valvular stenosis (AS) using
ECG (Electrocardiogram) images by the deep learning whose training ECG images are …

… a deep learning-based multi-label classification model with Gradient-weighted Class Activation
Mapping (Grad-CAM… The architecture of deep learning model was designed by ourselves …

… to develop explainable deep learning models, and … GradCAM, we propose Grad-CAM++
to provide better visual explanations of CNN model predictions (when compared to Grad-CAM), …

… Then, we build an embedding/gradweights database that is used for testing images. Variants
of Grad-CAM: In the above, we use GradCAM as the basic method to compute the grad-…