Identifying the probability of genetic mutations in lung cancer using predictive and prognostic biomarkers from histopathological images DOI Open Access
Lokeswari Venkataramana,

D. Venkata Vara Prasad,

G V N Akshay Varma

et al.

Medical Imaging Process & Technology, Journal Year: 2023, Volume and Issue: 6(1)

Published: Dec. 26, 2023

Background: Lung cancer is the highest deadliest disease and second largest being diagnosed worldwide. In age of precision medicine, determining a patient’s genetic status critical. Finding percentage gene mutation particular biomarker will help in targeted therapy patient at an early stage. Objective: Histopathology images are larger size which needs to be converted into smaller tiles for computational purpose. Deep Learning Techniques could applied on this huge number histopathological derive probability occurrence predictive prognostic biomarkers lung cancer. Methods: work, deep learning convolutional neural network (CNN) model (InceptionV3) trained histopathology obtained from The Cancer Genome Atlas (TCGA) accurately predict mutated genes adenocarcinoma. network-based predicts 10 major mutations percentage, i.e., EGFR, FAT1, FAT4, KEAP1, KRAS, LRP1B, NF1, SETBP1, STK11, TP53. Results: InceptionV3 predicted categorized as prognostic. yielded accuracy 82.36% cross entropy 37.62%. Conclusion: was with 82%. Prediction different CNN models like AlexNet ResNet can explored further.

Language: Английский

Advancements in deep learning for accurate classification of grape leaves and diagnosis of grape diseases DOI Creative Commons
İsmail Kunduracıoğlu, İshak Paçal

Journal of Plant Diseases and Protection, Journal Year: 2024, Volume and Issue: 131(3), P. 1061 - 1080

Published: March 26, 2024

Abstract Plant diseases cause significant agricultural losses, demanding accurate detection methods. Traditional approaches relying on expert knowledge may be biased, but advancements in computing, particularly deep learning, offer non-experts effective tools. This study focuses fine-tuning cutting-edge pre-trained CNN and vision transformer models to classify grape leaves diagnose leaf through digital images. Our research examined a PlantVillage dataset, which comprises 4062 images distributed across four categories. Additionally, we utilized the Grapevine consisting of 500 dataset is organized into five distinct groups, with each group containing 100 corresponding one types. The classes related diseases, namely Black Rot, Leaf Blight, Healthy, Esca leaves. On other hand, includes for recognition, specifically Ak, Alaidris, Buzgulu, Dimnit, Nazli. In experiments 14 17 models, learning demonstrated high accuracy distinguishing recognizing Notably, achieved 100% datasets, Swinv2-Base standing out. approach holds promise enhancing crop productivity early disease providing insights variety characterization agriculture.

Language: Английский

Citations

29
Advancing cancer diagnosis and treatment: integrating image analysis and AI algorithms for enhanced clinical practice DOI Creative Commons
Hamid Reza Saeidnia, Faezeh Firuzpour, Marcin Kozak

et al.

Artificial Intelligence Review, Journal Year: 2025, Volume and Issue: 58(4)

Published: Jan. 25, 2025

Language: Английский

Citations

4
Attention-guided generator with dual discriminator GAN for real-time video anomaly detection DOI
Rituraj Singh, Anikeit Sethi, Krishanu Saini

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 131, P. 107830 - 107830

Published: Jan. 13, 2024

Language: Английский

Citations

12
A computational pipeline towards large-scale and multiscale modeling of traumatic axonal injury DOI

Chaokai Zhang,

Lara Bartels,

Adam Clansey

et al.

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 171, P. 108109 - 108109

Published: Feb. 10, 2024

Language: Английский

Citations

6
Deep Learning-Based Automated Emotion Recognition Using Multi modal Physiological Signals and Time-Frequency Methods DOI

Sriram Kumar P,

Praveen Kumar Govarthan,

Abdul Aleem Shaik Gadda

et al.

IEEE Transactions on Instrumentation and Measurement, Journal Year: 2024, Volume and Issue: 73, P. 1 - 12

Published: Jan. 1, 2024

Language: Английский

Citations

5
Emerging pharmacotherapy trends in preventing and managing oral mucositis induced by chemoradiotherapy and targeted agents DOI
Margherita Gobbo,

Jamie K. Joy,

Helena Guedes

et al.

Expert Opinion on Pharmacotherapy, Journal Year: 2024, Volume and Issue: 25(6), P. 727 - 742

Published: April 12, 2024

The introduction of targeted therapy and immunotherapy has tremendously changed the clinical outcomes prognosis cancer patients. Despite innovative pharmacological therapies improved radiotherapy (RT) techniques, patients continue to suffer from side effects, which oral mucositis (OM) is still most impactful, especially for quality life. We provide an overview current advances in pharmacotherapy RT, relation their potential cause OM, less explored more recent literature reports related best management OM. have analyzed natural/antioxidant agents, probiotics, mucosal protectants healing coadjuvants, pharmacotherapies, immunomodulatory anticancer photobiomodulation impact technology. discovery precise pathophysiologic mechanisms CT RT-induced OM outlined that a multifactorial origin, including direct oxidative damage, upregulation immunologic factors, effects on flora. A persistent upregulated immune response, associated with factors patients' characteristics, may contribute severe long-lasting goal strategies conjugate individual patient, disease, therapy-related guide prevention or treatment. further high-quality research warranted, issue paramount future strategies.

Language: Английский

Citations

4
Multiscale information enhanced spatial-temporal graph convolutional network for multivariate traffic flow forecasting via magnifying perceptual scope DOI
Xinyu Zheng, Haidong Shao, Shen Yan

et al.

Engineering Applications of Artificial Intelligence, Journal Year: 2024, Volume and Issue: 136, P. 109010 - 109010

Published: July 22, 2024

Language: Английский

Citations

4
Advancements in Hyperspectral Imaging and Computer-Aided Diagnostic Methods for the Enhanced Detection and Diagnosis of Head and Neck Cancer DOI Creative Commons
I‐Chen Wu, Yen‐Chun Chen, Riya Karmakar

et al.

Biomedicines, Journal Year: 2024, Volume and Issue: 12(10), P. 2315 - 2315

Published: Oct. 11, 2024

Background/Objectives: Head and neck cancer (HNC), predominantly squamous cell carcinoma (SCC), presents a significant global health burden. Conventional diagnostic approaches often face challenges in terms of achieving early detection accurate diagnosis. This review examines recent advancements hyperspectral imaging (HSI), integrated with computer-aided (CAD) techniques, to enhance HNC Methods: A systematic seven rigorously selected studies was performed. We focused on CAD algorithms, such as convolutional neural networks (CNNs), support vector machines (SVMs), linear discriminant analysis (LDA). These are applicable the tissues. Results: The meta-analysis findings indicate that LDA surpasses other an accuracy 92%, sensitivity 91%, specificity 93%. CNNs exhibit moderate performance, 82%, 77%, 86%. SVMs demonstrate lowest 76% 48%, but maintain high level at 89%. Additionally, vivo superior performance when compared ex studies, reporting higher (81%), (83%), (79%). Conclusion: Despite these promising findings, persist, HSI’s external conditions, need for high-resolution high-speed imaging, lack comprehensive spectral databases. Future research should emphasize dimensionality reduction integration multiple machine learning models, development extensive libraries clinical utility diagnostics. underscores transformative potential HSI techniques revolutionizing diagnostics, facilitating more earlier detection, improving patient outcomes.

Language: Английский

Citations

3
Base on contextual phrases with cross-correlation attention for aspect-level sentiment analysis DOI
Chao Zhu, Benshun Yi, Laigan Luo

et al.

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 241, P. 122683 - 122683

Published: Nov. 23, 2023

Language: Английский

Citations

7
MAFNet: dual-branch fusion network with multiscale atrous pyramid pooling aggregate contextual features for real-time semantic segmentation DOI Creative Commons
Shan Zhao, Yunlei Wang, Xuan Wu

et al.

Complex & Intelligent Systems, Journal Year: 2024, Volume and Issue: 10(4), P. 5107 - 5126

Published: April 17, 2024

Abstract Currently, many real-time semantic segmentation networks aim for heightened accuracy, inevitably leading to increased computational complexity and reduced inference speed. Therefore, striking a balance between accuracy speed has emerged as crucial concern in this domain. To address these challenges, study proposes dual-branch fusion network with multiscale atrous pyramid pooling aggregate contextual features (MAFNet). The first key component, the semantics guide spatial-details module (SGSDM) not only facilitates precise boundary extraction fine-grained classification, but also provides semantic-based feature representation, thereby enhancing support spatial analysis decision boundaries. second (MSAPPM), is designed by combining dilation convolution operations at various rates. This design expands receptive field, aggregates rich information more effectively. further improve of generated dual-branch, bilateral (BFM) introduced. employs cross-fusion calculating weights weight relationship dual branches, achieving effective fusion. validate effectiveness proposed network, experiments are conducted on single A100 GPU. MAFNet achieves mean intersection over union (mIoU) 77.4% 70.9 FPS Cityscapes test dataset 77.6% mIoU 192.5 CamVid dataset. experimental results conclusively demonstrated that effectively strikes

Language: Английский

Citations

2