Cervical cancer diagnosis using very deep networks over different activation functions

KMA Adweb, N Cavus, B Sekeroglu - Ieee Access, 2021 - ieeexplore.ieee.org
Cancer prevention is mainly achieved by screening the transformation zones. Cervical pre-
cancerous stages can be seen in three different types, and all can transform into cancer …

[HTML][HTML] Attention-VGG16-UNet: a novel deep learning approach for automatic segmentation of the median nerve in ultrasound images

A Huang, L Jiang, J Zhang, Q Wang - Quantitative imaging in …, 2022 - ncbi.nlm.nih.gov
Background Ultrasonography—an imaging technique that can show the anatomical section
of nerves and surrounding tissues—is one of the most effective imaging methods to …

Why is everyone training very deep neural network with skip connections?

OK Oyedotun, K Al Ismaeil… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Recent deep neural networks (DNNs) with several layers of feature representations rely on
some form of skip connections to simultaneously circumnavigate optimization problems and …

Low-dose CT image denoising using residual convolutional network with fractional TV loss

M Chen, YF Pu, YC Bai - Neurocomputing, 2021 - Elsevier
In this work, we propose a Fractional-order Residual Convolutional Neural Network
(FRCNN) for Low-Dose CT (LDCT) denoising. As increasing the dose of radiation is harmful …

PE-USGC: Posture estimation-based unsupervised spatial gaussian clustering for supervised classification of near-duplicate human motion

H Iyer, H Jeong - IEEE Access, 2024 - ieeexplore.ieee.org
Near-duplicate human motion classification presents significant challenges due to the subtle
differences and high similarity between actions. This paper introduces a posture estimation …

Training very deep neural networks: Rethinking the role of skip connections

OK Oyedotun, K Al Ismaeil, D Aouada - Neurocomputing, 2021 - Elsevier
State-of-the-art deep neural networks (DNNs) typically consist of several layers of features
representations, and especially rely on skip connections to avoid the difficulty of model …

Transfer learning-based YOLOv3 model for road dense object detection

C Zhu, J Liang, F Zhou - Information, 2023 - mdpi.com
Stemming from the overlap of objects and undertraining due to few samples, road dense
object detection is confronted with poor object identification performance and the inability to …

AttentionLUNet: A hybrid model for Parkinson's disease detection using MRI brain

AR Palakayala, P Kuppusamy - IEEE Access, 2024 - ieeexplore.ieee.org
Magnetic Resonance Imaging (MRI) is a medical imaging method used to visualize the
brain's anatomy, evaluate its function, and identify any abnormalities or disorders without the …

注意力与多尺度特征融合的水培芥蓝花蕾检测.

夏红梅, 赵楷东, 江林桓, 刘园杰… - Transactions of the …, 2021 - search.ebscohost.com
准确辨识水培芥蓝花蕾特征是区分其成熟度, 实现及时采收的关键. 该研究针对自然环境下不同
品种与成熟度的水培芥蓝花蕾外形与尺度差异大, 花蕾颜色与茎叶相近等问题 …

Improving the capacity of very deep networks with maxout units

OK Oyedotun, D Aouada… - 2018 IEEE International …, 2018 - ieeexplore.ieee.org
Deep neural networks inherently have large representational power for approximating
complex target functions. However, models based on rectified linear units can suffer …