CNN Image Classification

Hybrid Attention-Based Residual Network for Image Classification

Abstract: Residual Attention Networks (RANs) are a class of Convolutional Neural Networks (CNNs) that integrate attention mechanisms into deep architectures. RANs employ stacked attention modules to ...

IEEE

A Novel Transformer-CNN Hybrid Deep Learning Architecture for Robust Broad-Coverage Diagnosis of Eye Diseases on Color Fundus Images

Abstract: We present a novel and robust deep-learning architecture that takes into account the pathological characteristics of eye diseases on color fundus images. The proposed hybrid architecture is ...

IEEE

Flattening Images Manifold at the Nishimori Point: Exploring Linearity in Polysemantic Embedding for Human-Aware Bayesian CNN Image Classification

Abstract: Efficient image classification often requires a balance between accuracy and computational complexity. In this paper, we propose a graph-based spectral embedding method that leverages ...

IEEE

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Hybrid Attention-Based Residual Network for Image Classification

A Novel Transformer-CNN Hybrid Deep Learning Architecture for Robust Broad-Coverage Diagnosis of Eye Diseases on Color Fundus Images

Flattening Images Manifold at the Nishimori Point: Exploring Linearity in Polysemantic Embedding for Human-Aware Bayesian CNN Image Classification

Multilevel Multimodal CNN–Transformer Fusion Network for Land Use and Land Cover Classification

Minimizing Model Size of CNN-Based Vehicle Make Recognition for Frontal Vehicle Images

tensorflow-keras-cnn-image-classification