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

Classification of Kidney Abnormalities in CT Images using Deep Learning

Abstract: The CT Kidney Dataset is a structured and medically significant collection of Computed Tomography (CT) scan images, curated for the i m p ro v em en t and growth of AI-predicted diagnostic ...
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

Deep Learning for Satellite Image Classification: A Comparative Analysis of CNN and ResNet-18

Abstract: With the ease of classifying land through satellite imaging, remote sensing has captured the Earth observation domain. Traditional methods for analyzing satellite images relied on manual ...
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

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 ...
IEEE

Skin Disease Detection Using CNN: 6th International Conference on Innovative Trends in Information Technology(ICITIIT-2025)

Abstract: Skin diseases and it's infectious diseases are the most common health issues, requiring quick and correct diagnosis for appropriate treatment. In this study to describe the uses of ...
IEEE

Automated Trash Detection and Classification Using CNN: A Deep Learning Approach

Abstract: A research project focuses on creating automated trash detection and classification through convolutional neural networks (CNNs) with an objective to improve waste management systems. The ...