News-Medical.Net

CNN-based system improves lung nodule detection and classification

Lung cancer remains the leading cause of cancer-related mortality worldwide. Early detection of pulmonary nodules is crucial for timely diagnosis and effective treatment.
EurekAlert!

Enhanced Pulmonary Nodule Detection and Classification Using Artificial Intelligence on LIDC-IDRI Data (IMAGE)

The proposed CNN-based system demonstrates the feasibility and robustness of deep learning for automatic lung nodule detection and classification. Despite strong results, the study acknowledges ...
Mirage News

AI Boosts Lung Nodule Detection, Classification on LIDC Data

Lung cancer remains the leading cause of cancer-related mortality worldwide. Early detection of pulmonary nodules is crucial for timely diagnosis and ...
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

Hybrid Integration of CNN and Kolmogorov–Arnold Networks for Multispectral Image Super-Resolution

Abstract: This letter proposes KAN-based multispectral image super-resolution method (KMSR), a novel deep learning framework for multispectral image (MSI) super-resolution (SR) that integrates ...

WiMi Releases Hybrid Quantum-Classical Neural Network (H-QNN) Technology for Efficient MNIST Binary Image Classification

Beijing, Feb. 06, 2026 (GLOBE NEWSWIRE) -- WiMi Releases Hybrid Quantum-Classical Neural Network (H-QNN) Technology for Efficient MNIST Binary Image Classification ...
IEEE

Enhanced EEG Image Classification Using ESRGAN and Decision Tree for Early Diagnosis of Neurological Disorders

Abstract: Neurological disorder diagnosis from EEG signals is hindered by low data resolution and limitations in feature extraction. In this work, the use of Enhanced Super-Resolution Generative ...
IEEE

Interpretable Deep Learning for Multiclass Psychological Disorder Classification Using CNN and TCAV Using fMRI

Abstract: In this paper a 3D convolutional neural network model is presented that has been improved with squeeze-and-excitation blocks and residual blocks to categorize functional MRI data across ...
IEEE

Lightweight CNN and Transfer Learning Techniques for Robust Small-Image Classification

Abstract: This study investigates the efficiency vs. accuracy trade-offs of these two approaches using the Fashion-MNIST benchmark. The study examined five models: LeNet-5 and an efficient CNN trained ...
IEEE

Generalized ECG Heartbeat Classification Using Time-Series Transformers

Abstract: This research investigates Time-Series Transformer architectures for Electrocardiogram (ECG) heartbeat classification, particularly focusing on their generalization capabilities towards new ...
IEEE

Self-Supervised Monocular Depth Estimation Using Hybrid CNN-VMamba Architecture

Abstract: Self-supervised monocular depth estimation (MDE) typically employs convolutional neural networks (CNNs) or Transformers to predict scene depth. However, CNNs struggle with long-range ...
IEEE

Hybrid CNN-Swin Framework for Detection and Classification of Potato Leaf Diseases

Abstract: Timely and accurate identification of plant diseases is essential for sustainable agricultural practices and food security. This study presents a deep learning-based diagnostic framework ...