Evolving medical image classification: a three-tiered framework combining MSPLnet and IRNet-VGG19 DOI

G. Annapoorani,

M. Palanisamy,

C. Heltin Genitha

et al.

Evolving Systems, Journal Year: 2024, Volume and Issue: 16(1)

Published: Dec. 5, 2024

Language: Английский

An attention-fused architecture for brain tumor diagnosis DOI

Arash Hekmat,

Zuping Zhang, Saif Ur Rehman Khan

et al.

Biomedical Signal Processing and Control, Journal Year: 2024, Volume and Issue: 101, P. 107221 - 107221

Published: Nov. 20, 2024

Language: Английский

Citations

12
DASNet: A Convolutional Neural Network with SE Attention Mechanism for ccRCC Tumor Grading DOI
Xiaoyi Yu, Donglin Zhu,

Hongjie Guo

et al.

Interdisciplinary Sciences Computational Life Sciences, Journal Year: 2025, Volume and Issue: unknown

Published: March 24, 2025

Language: Английский

Citations

0
Multi-modal global- and local- feature interaction with attention-based mechanism for diagnosis of Alzheimer’s disease DOI

Nana Jia,

Tong Jia,

Zhao Li

et al.

Biomedical Signal Processing and Control, Journal Year: 2024, Volume and Issue: 95, P. 106404 - 106404

Published: May 7, 2024

Language: Английский

Citations

3
Deep learning and radiomics-based approach to meningioma grading: exploring the potential value of peritumoral edema regions DOI
Zhuo Zhang,

Ying Miao,

Jixuan Wu

et al.

Physics in Medicine and Biology, Journal Year: 2024, Volume and Issue: 69(10), P. 105002 - 105002

Published: April 9, 2024

To address the challenge of meningioma grading, this study aims to investigate potential value peritumoral edema (PTE) regions and proposes a unique approach that integrates radiomics deep learning techniques.

Language: Английский

Citations

2
ADBNet: An Attention-Guided Deep Broad Convolutional Neural Network for the Classification of Breast Cancer Histopathology Images DOI Creative Commons

Musfequa Rahman,

Kaushik Deb, Pranab Kumar Dhar

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 133784 - 133809

Published: Jan. 1, 2024

Language: Английский

Citations

2
ConjunctiveNet: an improved deep learning-based conjunctive-eyes segmentation and severity detection model DOI

Seema Pahwa,

Amandeep Kaur, Poonam Dhiman

et al.

International Journal of Intelligent Computing and Cybernetics, Journal Year: 2024, Volume and Issue: 17(4), P. 783 - 804

Published: Aug. 16, 2024

Purpose The study aims to enhance the detection and classification of conjunctival eye diseases' severity through development ConjunctiveNet, an innovative deep learning framework. This model incorporates advanced preprocessing techniques utilizes a modified Otsu’s method for improved image segmentation, aiming improve diagnostic accuracy efficiency in healthcare settings. Design/methodology/approach ConjunctiveNet employs convolutional neural network (CNN) enhanced transfer learning. methodology integrates rescaling, normalization, Gaussian blur filtering contrast-limited adaptive histogram equalization (CLAHE) preprocessing. segmentation novel method. framework’s effectiveness is compared against five pretrained CNN architectures including AlexNet, ResNet-50, ResNet-152, VGG-19 DenseNet-201. Findings finds that significantly outperforms existing models detecting various stages conditions. demonstrated superior performance classifying four distinct – initial, moderate, high, severe healthy stage offering reliable tool enhancing screening diagnosis processes ophthalmology. Originality/value represents significant advancement automated diseases, particularly conjunctivitis. Its originality lies integration its comprehensive approach, which collectively capabilities. framework offers substantial value field by improving disease classification, thus aiding better delivery.

Language: Английский

Citations

2
Cross-attention guided loss-based deep dual-branch fusion network for liver tumor classification DOI
Rui Wang, Xiaoshuang Shi, Shuting Pang

et al.

Information Fusion, Journal Year: 2024, Volume and Issue: unknown, P. 102713 - 102713

Published: Sept. 1, 2024

Language: Английский

Citations

2
RELA_Net: Upper Airway CBCT Image Segmentation Model Based on Receptive Field Expansion and Large-Kernel Attention DOI Creative Commons
Hongyong Gao, Weibo Song, Shulin Cui

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 89713 - 89725

Published: Jan. 1, 2024

The structure of the upper airway is variable and complex due to its environmental physiological factors. Currently, doctors mainly rely on manual outlining segmentation from images. This method time-consuming relies heavily doctor's experience. To solve this problem, we propose a fully automatic model for Cone Beam Computed Tomography (CBCT) images based U-Net. receptive field expansion module (RFEM) used replace last three convolutional blocks encoder in original U-Net improve feature information extraction capability. And large kernel attention (LKA) added skip connection part dynamically adjust backbone, alleviate loss redundancy connection. dataset paper one created by us clinicians themselves, totaling 1345 CBCT Which were taken 53 patients with obstruction. imaging experts guided delineated label Experimental results show that IoU Dice score predicted RELA_Net network article test sets are 94.39% 97.10% respectively. Based prediction maps set images, proposed demonstrates an improvement comparison other models, particularly reducing over- under-segmentation airway. contributes improving diagnostic accuracy obstruction, thereby enhancing patient care treatment planning.

Language: Английский

Citations

0
CytoNet: an efficient dual attention based automatic prediction of cancer sub types in cytology studies DOI Creative Commons
Naveed Ilyas,

Farhat Naseer,

Anwar A. Khan

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Oct. 28, 2024

Computer-assisted diagnosis (CAD) plays a key role in cancer or screening. Whereas, current CAD performs poorly on whole slide image (WSI) analysis, and thus fails to generalize well. This research aims develop an automatic classification system distinguish between different types of carcinomas. Obtaining rich deep features multi-class while achieving high accuracy is still challenging problem. The detection cancerous cells WSI are quite due the misclassification normal lumps cells. cluttering, occlusion, irregular cell distribution. Researchers past mostly obtained hand-crafted neglecting above-mentioned challenges which led reduction accuracy. To mitigate this problem we proposed efficient dual attention-based network (CytoNet). composed two main modules (i) Efficient-Net (ii) Dual Attention Module (DAM). capable obtaining higher enhancing efficiency as compared existing Convolutional Neural Networks (CNNs). It also useful obtain most generic it has been trained ImageNet. Whereas DAM very robust attention targeted negating background. In way, combination module robust, intrinsic comparable performance. Further, evaluated well-known datasets Our generated thyroid dataset Mendeley Cervical (Hussain Data Brief, 2019) with enhanced performance their counterparts. CytoNet demonstrated 99% rate comparison its counterpart. precision, recall, F1-score values achieved 0.992, 0.985, 0.977, respectively. code implementation available GitHub. https://github.com/naveedilyas/CytoNet-An-Efficient-Dual-Attention-based-Automatic-Prediction-of-Cancer-Sub-types-in-Cytol.

Language: Английский

Citations

0
Quaternion Squeeze and Excitation Networks: Mean, Variance, Skewness, Kurtosis As One Entity DOI

Mohamed Amine Mezghich,

Dorsaf Hmida,

Slim Mhiri

et al.

Lecture notes in computer science, Journal Year: 2024, Volume and Issue: unknown, P. 174 - 189

Published: Nov. 29, 2024

Language: Английский

Citations

0