IEEE

Hybrid CNN-ViT Model for Intracranial Hemorrhage Detection and Classification in DICOM Images

Abstract: Intracranial hemorrhage (ICH) refers to bleeding within the brain, a global concern that underscores the im-portance of early detection. ICH is typically detected using computed tomography ...
IEEE

Hi-RWKV: Hierarchical RWKV Modeling for Hyperspectral Image Classification

Abstract: Hyperspectral image (HSI) classification demands models that can jointly capture long-range spatial relations and high-dimensional spectral structures while remaining scalable to large ...
IEEE

Fully Perturbed Self-Ensemble Framework using Cascaded Parallel CNN-Transformer for Semi-Supervised Medical Image Segmentation

Abstract: Semi-supervised learning (SSL) has achieved remarkable progress in the field of medical image segmentation (MIS), but it still faces two main challenges. First, the consistency learning ...
IEEE

Hyperparameter Optimization of CNN Models for Plant Leaf Disease Classification Using Optuna

Abstract: Leaf diseases are a major challenge for agricultural productivity, requiring accurate and efficient detection methods. This research presents an effective method for multi-class ...
IEEE

Transforming Disability Into Ability: An Explainable Vision-to-Voice Image Captioning Framework Using Transformer Models and Edge Computing

Abstract: Image captioning is an emerging field at the intersection of computer vision and natural language processing (NLP). It has shown great potential to enhance accessibility by automatically ...
IEEE

BreNet: Attention-Enhanced Multi-Scale CNN Framework for Breast Cancer Classification in Histopathological Images

Abstract: Breast cancer remains a leading cause of mortality among women worldwide, emphasizing the critical importance of early and accurate detection in improving patient outcomes and treatment ...
IEEE

Brain Cancer MRI Classification using CNN Architectures with Grid Search Enhanced with Coordinate Ascent Optimization

Abstract: Magnetic resonance imaging (MRI) is an important tool for brain cancer diagnosis and classification. Combined with modern convolutional neural network (CNN) technology, it can effectively ...
IEEE

Spectral-Spatial Dual-Branch Selective Fusion Enhanced Transformer for Hyperspectral Image Classification

Abstract: In recent years, deep learning approaches that integrate convolutional neural networks (CNNs) with Transformer architectures have greatly enhanced the accuracy of hyperspectral image (HSI) ...
IEEE

A Hybrid CNN-Transformer Knowledge Transfer Technique for Lightweight Skin Lesion Classification using Dermoscopic Image

Abstract: Using dermoscopic images for the classification of skin lesion is crucial for early skin cancer detection, but resource limitations hinder complex deep learning model applications in ...
IEEE

Optimized Hybrid Model Integrating CNN and Image Processing for Lung Cancer

Abstract: The number of deaths related to cancer, and particularly the well-known lung cancer, remains the leading cause of morbidity in most regions of the globe, and early detection of the disease ...
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 ...
IEEE

Identification and Classification of Medicinal Plants Using Resnet V2 CNN Architecture

Abstract: Identifying medicinal plants is crucial in herbal medicine, pharmaceutical research, and plant taxonomy. Conventional manual classification techniques tend to be errorprone and ...