Beebom

What is CNN in Deep Learning? The AI Behind Computer Vision

CNN in deep learning is a special type of neural network that can understand images and visual information. It works just like human vision: first it detects edges, lines and then recognizes faces and ...
Frontiers

Automated predictive framework using AI and deep learning approaches for early detection and classification of liver cancer

1 Department of General Surgery, Shangyu People’s Hospital of Shaoxing, Shaoxing, Zhejiang, China 2 Center of Gallbladder Disease, Shanghai East Hospital, Institute of Gallstone Disease, School of ...
IEEE

Multi-Branch Deep Learning Architecture for Satellite Image Classification Using ViTNet-FCN With Transfer Learning

Satellite image classification plays a vital role in land cover mapping, resource management, and environmental monitoring. While recent advances in deep learning have improved performance, single ...
Scientific Research Publishing

Hem-CNN: An Efficient Intracranial Hemorrhage Detection Model Using Explainable Deep Learning in Head CT Scan Images ()

1 Department of Computer Science, American International University-Bangladesh (AIUB), Dhaka, Bangladesh. 2 Department of Electrical and Electronics Engineering, American International ...
C&EN

Deep Learning-Based Shape Classification for Hyperspectral-Imaged Microplastics

Article subjects are automatically applied from the ACS Subject Taxonomy and describe the scientific concepts and themes of the article. Figure 1 illustrates the overall workflow of the hyperspectral ...
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 ...