DenseIncepS115: a novel network-level fusion framework for Alzheimer's disease prediction using MRI images DOI Creative Commons

Fatima Rauf,

Muhammad Attique Khan,

Ghassen Ben Brahim

et al.

Frontiers in Oncology, Journal Year: 2024, Volume and Issue: 14

Published: Dec. 3, 2024

One of the most prevalent disorders relating to neurodegenerative conditions and dementia is Alzheimer's disease (AD). In age group 65 older, prevalence increasing. Before symptoms showed up, had grown a severe stage resulted in an irreversible brain disorder that not treatable with medication or other therapies. Therefore, early prediction essential slow down AD progression. Computer-aided diagnosis systems can be used as second opinion by radiologists their clinics predict using MRI scans. this work, we proposed novel deep learning architecture named DenseIncepS115for for from The based on Inception Module Self-Attention (InceptionSA) Dense (DenseSA). Both modules are fused at network level depth concatenation layer. hyperparameters initialized Bayesian Optimization, which impacts better selected datasets. testing phase, features extracted layer, further optimized Catch Fish Optimization (CFO) algorithm passed shallow wide neural classifiers final prediction. addition, DenseIncepS115 interpreted through Lime Gradcam explainable techniques. Two publicly available datasets were employed experimental process: ADNI classes MRI. On both datasets, obtained accuracy 99.5% 98.5%, respectively. Detailed ablation studies comparisons state-of-the-art techniques show outperforms.

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

11
The Diagnostic Classification of the Pathological Image Using Computer Vision DOI Creative Commons

Yasunari Matsuzaka,

Ryu Yashiro

Algorithms, Journal Year: 2025, Volume and Issue: 18(2), P. 96 - 96

Published: Feb. 8, 2025

Computer vision and artificial intelligence have revolutionized the field of pathological image analysis, enabling faster more accurate diagnostic classification. Deep learning architectures like convolutional neural networks (CNNs), shown superior performance in tasks such as classification, segmentation, object detection pathology. has significantly improved accuracy disease diagnosis healthcare. By leveraging advanced algorithms machine techniques, computer systems can analyze medical images with high precision, often matching or even surpassing human expert performance. In pathology, deep models been trained on large datasets annotated pathology to perform cancer diagnosis, grading, prognostication. While approaches show great promise challenges remain, including issues related model interpretability, reliability, generalization across diverse patient populations imaging settings.

Language: Английский

Citations

0
Variable Step Sizes for Iterative Jacobian-Based Inverse Kinematics of Robotic Manipulators DOI Creative Commons
Jacinto Colan, Ana Davila, Yasuhisa Hasegawa

et al.

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 87909 - 87922

Published: Jan. 1, 2024

Language: Английский

Citations

3
The human-centric framework integrating knowledge distillation architecture with fine-tuning mechanism for equipment health monitoring DOI
Jr-Fong Dang, Tzu‐Li Chen, Hung‐Yi Huang

et al.

Advanced Engineering Informatics, Journal Year: 2025, Volume and Issue: 65, P. 103167 - 103167

Published: Feb. 6, 2025

Language: Английский

Citations

0
An improved attentive residue multi-dilated network for thermal noise removal in magnetic resonance images DOI
Bowen Jiang, Tao Yue, Xuemei Hu

et al.

Image and Vision Computing, Journal Year: 2024, Volume and Issue: 150, P. 105213 - 105213

Published: Aug. 10, 2024

Language: Английский

Citations

1
Beyond Algorithms: The Impact of Simplified CNN Models and Multifactorial Influences on Radiological Image Analysis DOI Creative Commons
Saber Mohammadi, Abhinita Mohanty, Shady Saikali

et al.

medRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 16, 2024

Abstract This paper demonstrates that simplified Convolutional Neural Network (CNN) models can outperform traditional complex architectures, such as VGG-16, in the analysis of radiological images, particularly datasets with fewer samples. We introduce two adopted CNN LightCnnRad and DepthNet, designed to optimize computational efficiency while maintaining high performance. These were applied nine image datasets, both public in-house, including MRI, CT, X-ray, Ultrasound, evaluate their robustness generalizability. Our results show these achieve competitive accuracy lower costs resource requirements. finding underscores potential streamlined clinical settings, offering an effective efficient alternative for analysis. The implications medical diagnostics are significant, suggesting simpler, more algorithms deliver better performance, challenging prevailing reliance on transfer learning models. complete codebase detailed architecture along step-by-step instructions, accessible our GitHub repository at https://github.com/PKhosravi-CityTech/LightCNNRad-DepthNet .

Language: Английский

Citations

1
Quantum neural network-assisted learning for small medical datasets: a case study in emphysema detection DOI

Safura Oviesi,

Mohammad Jafar Tarokh, Milad Momeni

et al.

The Journal of Supercomputing, Journal Year: 2024, Volume and Issue: 81(1)

Published: Dec. 18, 2024

Language: Английский

Citations

1
A Classifier Model Using Fine-Tuned Convolutional Neural Network and Transfer Learning Approaches for Prostate Cancer Detection DOI Creative Commons
Murat Sarıateş, Erdal Özbay

Applied Sciences, Journal Year: 2024, Volume and Issue: 15(1), P. 225 - 225

Published: Dec. 30, 2024

Background: Accurate and reliable classification models play a major role in clinical decision-making processes for prostate cancer (PCa) diagnosis. However, existing methods often demonstrate limited performance, particularly when applied to small datasets binary problems. Objectives: This study aims design fine-tuned deep learning (DL) model capable of classifying PCa MRI images with high accuracy evaluate its performance by comparing it various DL architectures. Methods: In this study, basic convolutional neural network (CNN) was developed subsequently optimized using techniques such as L2 regularization, Tanh activation, dropout, early stopping enhance performance. Additionally, pyramid-type CNN architecture designed simultaneously both fine details broader structures combining low- high-resolution information through feature maps extracted from different layers. approach enabled the learn complex features more effectively. For comparison, enhanced pyramid (FT-EPN) benchmarked against Vgg16, Vgg19, Resnet50, InceptionV3, Densenet121, Xception, which were trained transfer (TL) techniques. It also compared next-generation vision transformer (ViT) MaxViT-v2. Results: The achieved an rate 96.77%, outperforming pre-trained TL like ViT Among models, Vgg19 highest at 92.74%. 93.55%, while MaxViT-v2 95.16%. Conclusions: presents FT-EPN classification, offering reference solution future research. provides significant advantages terms simplicity has been evaluated effective applications.

Language: Английский

Citations

1
Gaba: A Generic Anti-Compression Backdoor Attack Using the Characteristic of Image Compression DOI
Wenjie Wang, Honglong Chen, Junjian Li

et al.

Published: Jan. 1, 2024

Language: Английский

Citations

0
Optimal Prompting in SAM for Few-Shot and Weakly Supervised Medical Image Segmentation DOI

Lara Siblini,

Gustavo Andrade-Miranda,

Kamilia Taguelmimt

et al.

Lecture notes in computer science, Journal Year: 2024, Volume and Issue: unknown, P. 103 - 112

Published: Sept. 28, 2024

Language: Английский

Citations

0