IEEE

A Hybrid Graph Neural Network and Neural ODE Model to Intrusion Detection in Dynamic Network Topologies

Abstract: The adaptive nature of evolving cyber-threats, particularly in Internet of Things (IoT) and Software-Defined Networking (SDN) environments, necessitates intrusion detection systems (IDS) ...
IEEE

Early Lung Cancer Detection Using a Hybrid Model

Abstract: This paper addresses the global health concern of early detection for increased treatment effectiveness, this research proposes a novel automated technique for lung cancer detection that ...
IEEE

Early-Stage Invasive Breast Cancer Detection Using Deep CNN Feature Embedding and Radiomic Texture Analysis Techniques

Abstract: This research proposes a high-performance hybrid framework for early-stage invasive breast cancer detection by integrating deep convolutional neural embeddings with handcrafted radiomic ...
IEEE

Enhancing Breast Cancer Detection Using Multistage Transformer with Positional Encoding and Feature Fusion

Abstract: Breast cancer (BC) remains the most common cancer among women worldwide. However, early detection and treatment have significantly improved the disease's prognosis and decreased mortality ...
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

Confidence-Weighted Semi-Supervised Learning for Skin Lesion Segmentation Using Hybrid CNN-Transformer Networks

Abstract: Automated skin lesion segmentation through dermoscopic analysis is essential for early skin cancer detection; however, it remains challenging because of the limited annotated training data.
IEEE

Enhancing Lung Cancer Detection Using Pretrained Deep Neural Network Models

Abstract: This paper presents a new deep learning framework, the Attentive Fusion Network (AF-Net), to improve early lung cancer diagnosis from CT scans. We tackle the limitations of standard hybrid ...
IEEE

Early-Stage Lung Cancer Detection Using a Hybrid CNN-DBN-SDAE Deep Learning Model

Abstract: Lung cancer is still one of the leading causes of cancer mortality worldwide, and late-stage detection severely decrease survival rates. Early detection is essential to enhance patient ...
IEEE

Augmenting Pathology in Women's Health with a CNN-Powered Hybrid Deep Learning Model for Accurate Cervical Cancer Prediction and Diagnosis

Abstract: Globally, cervical cancer stands as the fourth leading cancer diagnosis in women, yet it is still largely preventable and treatable through early detection and effective screening, as ...