Brain tumor diagnosis in MRI scans images using Residual/Shuffle Network optimized by augmented Falcon Finch optimization DOI Creative Commons

Xiaohang Guo,

Tianyi Liu, Qinglong Chi

et al.

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

Published: Nov. 13, 2024

Brain tumor diagnosis is an important task in prognosing and treatment planning of the patients with brain cancer. meantime, using Magnetic Resonance Imaging (MRI) as a commonly used non-invasive imaging technique provide experts helpful view for detecting tumors. While deep learning methods have shown significant success analyzing medical images, they often require careful design architecture tuning hyperparameters to achieve optimal results. This study presents new approach diagnosing tumors MRI scans learning, focusing on Residual/Shuffle Networks. The designed network structures offer efficient results when compared traditional models. To enhance proposed classification, modified metaheuristic algorithm named Augmented Falcon Finch Optimization (AFFO) introduced. AFFO utilizes bio-inspired principles effectively search best hyperparameter configurations, thereby enhancing reliability accuracy model. performance method evaluated standard dataset existing techniques, including ResNet, AlexNet, VGG-16, Inception V3, U-Net illustrate effectiveness combining Networks diagnosis.

Language: Английский

Integrating Metal–Phenolic Networks-Mediated Separation and Machine Learning-Aided Surface-Enhanced Raman Spectroscopy for Accurate Nanoplastics Quantification and Classification DOI
Haoxin Ye, Shiyu Jiang,

Yan Yan

et al.

ACS Nano, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 16, 2024

Increasing accumulation of nanoplastics across ecosystems poses a significant threat to both terrestrial and aquatic life. Surface-enhanced Raman scattering (SERS) is an emerging technique used for detection. However, the identification classification using SERS faces challenges regarding sensitivity accuracy as are sparsely dispersed in environment. Metal-phenolic networks (MPNs) have potential rapidly concentrate separate various types sizes nanoplastics. combined with machine learning may improve prediction accuracy. Herein, we report integration MPNs-mediated separation learning-aided methods accurate high-precision quantification nanoplastics, which tailored include complete region characteristic peaks diverse contrast traditional manual analysis spectra on singular peak. Our customized system (e.g., outlier detection, classification, quantification) allows detectable (accuracy 81.84%), > 97%), sensitive (polystyrene (PS), poly(methyl methacrylate) (PMMA), polyethylene (PE), poly(lactic acid) (PLA)) down ultralow concentrations (0.1 ppm) well 92%) nanoplastic mixtures at subppm level. The effectiveness this approach substantiated by its ability discern between different detect samples natural water systems.

Language: Английский

Citations

6
A hybrid multi-optimizer approach using CNN and GB for accurate prediction of citrus fruit diseases DOI Creative Commons

Lawrence Kujur,

V. B. Gupta, Abhinav Singhal

et al.

Deleted Journal, Journal Year: 2025, Volume and Issue: 7(3)

Published: Feb. 18, 2025

Language: Английский

Citations

0
VNLU-Net: Visual Network with Lightweight Union-net for Acute Myeloid Leukemia Detection on Heterogeneous Dataset DOI

Rabul Saikia,

Roopam Deka,

Anupam Sarma

et al.

Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 107, P. 107840 - 107840

Published: March 29, 2025

Language: Английский

Citations

0
Leveraging ensemble convolutional neural networks and metaheuristic strategies for advanced kidney disease screening and classification DOI Creative Commons
Abeer Saber,

Esraa Hassan,

Samar Elbedwehy

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 11, 2025

Abstract To address the public health issue of renal failure and global shortage nephrologists, an AI-based system has been developed to automatically identify kidney diseases. Recent advancements in machine learning, deep learning (DL), artificial intelligence (AI) have unlocked new possibilities healthcare. By harnessing these technologies, we can analyze data gain insights into symptoms patterns, ultimately facilitating remote patient care. create diagnosis for disease, this paper focused on three major categories diseases: stones, cysts, tumors, which were collected annotated 12,446 computed tomography (CT) whole abdomen urogram images. effectively aid automatic identification diseases, a novel DL model built transfer-learning (TL) technology is implemented work. models are designed focus problems, whereas TL uses knowledge acquired while resolving one another pertinent issue. The proposed combines multiple improve overall performance by leveraging strengths different architectures, ensembles enhance accuracy, robustness, generalization. It enhances features extracted from MobileNet-V2, ResNet50, EfficientNet-B0 networks using metaheuristic algorithms bidirectional long-short-term memory (Bi-LSTM) CT image. MobileNetV2, hyperparameters optimized modified grey wolf optimization (GWO) approach better performance. suggested model’s measured five assessment metrics: sensitivity, specificity, precision, area under ROC curve (AUC) achieved 99.85% 99.8% 99.3% 98.1% 1.0 AUC.

Language: Английский

Citations

0
Advancing Breast Cancer Diagnosis: Integrating Deep Transfer Learning and U-Net Segmentation for Precise Classification and Delineation of Ultrasound Images DOI Creative Commons
Divine Senanu Ametefe, Dah John,

Abdulmalik Adozuka Aliu

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 105047 - 105047

Published: April 1, 2025

Language: Английский

Citations

0
The Intersection of AI, Cloud Computing, and Healthcare DOI
Wael A. Awad, Amena Mahmoud

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 379 - 404

Published: March 28, 2025

The convergence of Artificial Intelligence (AI) and Cloud Computing has ushered in a new era innovation across various industries, including healthcare. AI, with its ability to analyze vast datasets, identify patterns, make intelligent decisions, offers transformative potential for improving patient outcomes enhancing healthcare efficiency. Computing, on the other hand, provides scalable flexible infrastructure storing, processing, accessing data, enabling seamless collaboration among professionals development innovative applications. This overview presents comprehensive intersection AI healthcare, exploring their applications, benefits, challenges, ethical considerations. survey will delve into aspects cloud computing adoption usage, challenges opportunities, future trends, expectations.

Language: Английский

Citations

0
A Historical Perspective on Artificial Intelligence in Applied Life Sciences DOI
Tamer Z. Emara,

Esraa Hassan,

Samar Elbedwehy

et al.

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 49 - 86

Published: March 28, 2025

This chapter explores the transformative impact of Artificial Intelligence (AI) on applied life sciences, with a focus environmental science. It begins by tracing theoretical foundations AI from early pioneers like Alan Turing and John McCarthy, then discusses its evolution through development expert systems machine learning techniques. The highlights AI's significant contributions to monitoring, climate change prediction, biodiversity conservation, showcasing how enhances our understanding management complex systems. also examines integration satellite data sensor networks address challenges. Concluding future directions, addresses emerging trends ethical considerations, emphasizing role in supporting sustainable goals. overview provides foundational context for sciences sets stage exploring broader applications subsequent chapters.

Language: Английский

Citations

0
AI-Driven Laboratory Automation DOI
Amena Mahmoud, Wael A. Awad

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 361 - 378

Published: March 28, 2025

The integration of artificial intelligence (AI) into laboratory automation systems is revolutionizing the drug discovery process by enhancing efficiency and accuracy in experimental phases. By leveraging machine learning algorithms robotic systems, researchers can achieve higher throughput compound screening, optimize designs, reduce human error. A case study was discussed that demonstrated successful applications AI settings, highlighting advancements high-throughput data analysis, predictive modeling. Additionally, we address challenges associated with implementing automation, including integration, system interoperability, need for skilled personnel.

Language: Английский

Citations

0
Optimized ensemble deep learning approach for accurate breast cancer diagnosis using transfer learning and grey wolf optimization DOI
Esraa Hassan, Abeer Saber, Shaker El–Sappagh

et al.

Evolving Systems, Journal Year: 2025, Volume and Issue: 16(2)

Published: April 29, 2025

Language: Английский

Citations

0
Addressing cross-population domain shift in chest X-ray classification through supervised adversarial domain adaptation DOI Creative Commons
Ramli Musa, Rajesh S. Prasad,

Monica Hernandez

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 3, 2025

Language: Английский

Citations

0