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Introducing Feature Attention Module on Convolutional Neural Network for Diabetic Retinopathy Detection
The simultaneous learning of attention weights for the features and thereupon the combination of attention-modulated features within the feature attention block facilitates the network's ability to focus on relevant information while reducing the impact of noisy or irrelevant features.
Transfer-Ensemble Learning based Deep Convolutional Neural Networks for Diabetic Retinopathy Classification
This article aims to classify diabetic retinopathy (DR) disease into five different classes using an ensemble approach based on two popular pre-trained convolutional neural networks: VGG16 and Inception V3.
T-Fusion Net: A Novel Deep Neural Network Augmented with Multiple Localizations based Spatial Attention Mechanisms for Covid-19 Detection
The proposed T-Fusion Net and the homogeneous ensemble model exhibit better performance, as compared to other state-of-the-art methods, achieving accuracy of 97. 59% and 98. 4%, respectively.
Automated COVID-19 CT Image Classification using Multi-head Channel Attention in Deep CNN
The rapid spread of COVID-19 has necessitated efficient and accurate diagnostic methods.
