IEEE

Explainable Brain Tumor Classification using CNN and GradCAM

Abstract: One of the most critical neurological conditions is Brain tumors, timely and correct diagnosis is needed for effective treatment. Advances in neuroimaging technology such as MRI, limitations ...
European Medical Journal

Lightweight Neural Networks Improve Lung Cancer Diagnosis

Lightweight convolutional neural networks improved lung cancer classification accuracy in histopathological images while ...
IEEE

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

Abstract: In remote sensing (RS), convolutional neural networks (CNNs) are well-recognized for their spatial–spectral feature extraction capabilities, whereas vision transformers (ViTs), which ...
IEEE

CNN-Based Plant Disease Classification: An Image Analysis Approach

Abstract: The agriculture industry faces significant challenges in maintaining sustainable plant growth while combating diseases that threaten crops. Traditional disease prevention methods rely on ...
IEEE

SwinCNN+OE: A Swin Transformer and CNN Architecture for Breast Histopathology Classification with OOD and Grad-CAM Integration

Abstract: This study aimed to design and evaluate a fusion deep learning architecture (SwinCNN + OE) for robust and interpretable breast cancer classification using histopathological images. The ...
Frontiers

CNNAttLSTM: an attention-enhanced CNN–LSTM architecture for high-precision jackfruit leaf disease classification

Introduction: Jackfruit cultivation is highly affected by leaf diseases that reduce yield, fruit quality, and farmer income. Early diagnosis remains challenging due to the limitations of manual ...
IEEE

Decoding Image classes from EEG Based on CNN-LSTM-Attention Architecture

Abstract: The electroencephalograph (EEG) records the electrical activity of the human brain. Decoding the class of visual stimuli from EEG has always been a key focus in Brain-Computer Interface (BCI ...
IEEE

A Multi-Scale Attention Architecture by Fusing Graph Convolutional Network and CNN for Hyperspectral Image Classification

Abstract: Hyperspectral image (HSI) classification has been advanced by convolutional and graph convolutional networks (CNNs and GCNs). While CNNs excel at extracting local features, GCNs capture ...