Frontiers

Novel deep learning for multi-class classification of Alzheimer’s in disability using MRI datasets

Introduction: Alzheimer’s disease (AD) is one of the most common neurodegenerative disabilities that often leads to memory loss, confusion, difficulty in language and trouble with motor coordination.
IEEE

IMed-CNN: Ensemble Learning Approach With Systematic Model Dropout for Enhanced Medical Image Classification Using Image Channels and Pixel Intervals

Abstract: To obtain light ensemble model through clearly explained effective ensemble member selection and finding data representation in various valuable forms are major challenges in medical image ...
IEEE

Efficient Document Image Classification Using Deep Learning Enhance with CNN Architectures

Abstract: Document image classification has a significant difficulty for the retrieval of digital documents and systems management in recent years. The main goal of this study is to investigate the ...
Frontiers

Enhanced hyperspectral image classification for coastal wetlands using a hybrid CNN-transformer approach with cross-attention mechanism

1 State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research (IGSNRR), Chinese Academy of Sciences (CAS), Beijing, China 2 ...
The American Journal of Managed Care

Optimized Deep Learning Model Classifies Smartphone Images of Malignant Skin Lesions

Across various classes, the researchers identified a model that, when coupled with appropriate data augmentation and optimization partner, may help aid in the detection of skin cancer. Using an ...
GitHub

Gaserzaghloul/Sports-Image-Classification-Using-CNN

Built a deep CNN for multi-class sports image classification using a Kaggle dataset. Tuned hyperparameters (layers, dropout, optimizer, batch size, learning rate, weight decay) and trained with early ...