TL;DR
Node-U-Net, an ultra-lightweight attention-guided encoder-decoder architecture, is introduced as a highly scalable solution for real-time AI-assisted lung cancer screening, making it viable for integration into resource-constrained clinical environments and edge devices.
Accurate multi-class segmentation of brain tumors from T1-weighted contrast-enhanced MRI is essential for surgical planning and treatment monitoring, yet existing high-performance architectures exceed 30 M parameters, limiting deployment in resource-constrained clinical settings. This work introduces A3Net, a lightweig...
Reliable classification of lung cancer from chest CT scans is essential for early diagnosis, yet most deep learning (DL) models lack an anatomical basis and robust, quantitatively validated interpretability, limiting clinical adoption. This study aims to develop and validate a lung-aware, dual-branch DL framework that...
This study develops a lightweight Convolutional Neural Network model for pneumothorax detection in chest X-rays, targeting resource-limited healthcare environments, and bridges the gap between deep learning and practical medical applications, particularly in underserved regions.
Early and accurate analysis of breast cancer is critical for improving patient outcomes. Ultrasound imaging is widely used for breast tumor screening due to its safety, accessibility, and low cost. We propose DGTN (Diffused Graph-Transformer Network), a lightweight, end-to-end deep learning model that jointly performs...
Accurate classification of pulmonary nodules is essential for early lung cancer diagnosis, yet the limited spatial information provided by a single computed tomography slice may hinder the characterization of complex nodule morphology. Although multi-view strategies can capture complementary anatomical information, con...
To address the challenges of hyperparameter sensitivity and loss of fine details in medical image segmentation, particularly in lung-field segmentation from chest X-ray images, this study introduces a hybrid neural network architecture named UNet-ResNet50-ACO. The proposed framework integrates the Ant Colony Optimizati...
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