Skin Cancer Diagnosis Using VGG16 and Transfer Learning: Analyzing the Effects of Data Quality over Quantity on Model Efficiency DOI Creative Commons

Khamsa Djaroudib,

Pascal Lorenz,

Rime Belkacem Bouzida

и другие.

Applied Sciences, Год журнала: 2024, Номер 14(17), С. 7447 - 7447

Опубликована: Авг. 23, 2024

The recent increase in the prevalence of skin cancer, along with its significant impact on individuals’ lives, has garnered attention many researchers field deep learning models, especially following promising results observed using these models medical field. This study aimed to develop a system that can accurately diagnose one three types cancer: basal cell carcinoma (BCC), melanoma (MEL), and nevi (NV). Additionally, it emphasizes importance image quality, as studies focus quantity images used learning. In this study, transfer was employed pre-trained VGG-16 model alongside dataset sourced from Kaggle. Three were trained while maintaining same hyperparameters script ensure fair comparison. However, data train each varied observe specific effects hypothesize about quality within highest validation score selected for further testing separate test dataset, which had not seen before, evaluate model’s performance accurately. work contributes existing body research by demonstrating critical role enhancing diagnostic accuracy, providing comprehensive evaluation cancer detection offering insights guide future improvements

Язык: Английский

Reviewing the role of AI in environmental monitoring and conservation: A data-driven revolution for our planet DOI Creative Commons

Onyebuchi Nneamaka Chisom,

Preye Winston Biu,

Aniekan Akpan Umoh

и другие.

World Journal of Advanced Research and Reviews, Год журнала: 2024, Номер 21(1), С. 161 - 171

Опубликована: Янв. 4, 2024

The rapid increase in human activities is causing significant damage to our planet's ecosystems, necessitating innovative solutions preserve biodiversity and counteract ecological threats. Artificial Intelligence (AI) has emerged as a transformative force, providing unparalleled capabilities for environmental monitoring conservation. This research paper explores the applications of AI ecosystem management, including wildlife tracking, habitat assessment, analysis, natural disaster prediction. AI's role conservation includes resource conservation, species identification. algorithms analyze camera trap footage, drone imagery, GPS data identify estimate population sizes, leading improved anti-poaching efforts enhanced protection diverse species. Habitat assessment involve AI-powered image which aids assessing forest health, detecting deforestation, identifying areas need restoration. Biodiversity analysis identification are achieved through that acoustic recordings, DNA (eDNA), footage. These innovations different species, assess levels, even discover new or endangered flood prediction systems provide early warnings, empowering communities with better preparedness evacuation efforts. Challenges, such quality availability, algorithmic bias, infrastructure limitations, acknowledged opportunities growth improvement. In policy regulation, advocates clear frameworks prioritizing privacy security, transparency, equitable access. Responsible development ethical use emphasized foundational pillars, ensuring integration into aligns principles fairness, societal benefit.

Язык: Английский

Процитировано

40
Particle Swarm Optimization in Biomedical Technologies DOI
S. Kannadhasan,

R. Nagarajan

Advances in healthcare information systems and administration book series, Год журнала: 2024, Номер unknown, С. 220 - 238

Опубликована: Фев. 14, 2024

This chapter explores particle swarm optimization (PSO) in the rapidly evolving landscape of biomedical technologies. The study begins by introducing fundamental principles PSO, emphasizing its advantages addressing complex problems common applications. authors delve into innovative uses PSO various fields, including image enhancement, data clustering, and drug development, highlighting how contributes to more accurate diagnoses, efficient treatment plans, streamlined research methodologies. Significantly, this identifies emerging opportunities where can be further leveraged, particularly personalized medicine predictive health analytics, suggesting a roadmap for future development. By combining theoretical insights with practical examples, work aims provide comprehensive overview PSO's role advancing technologies, offering valuable perspectives researchers, practitioners, policymakers field.

Язык: Английский

Процитировано

26
Interpretable Radiomic Signature for Breast Microcalcification Detection and Classification DOI Creative Commons
Francesco Prinzi, Alessia Angela Maria Orlando, Salvatore Gaglio

и другие.

Deleted Journal, Год журнала: 2024, Номер 37(3), С. 1038 - 1053

Опубликована: Фев. 13, 2024

Abstract Breast microcalcifications are observed in 80% of mammograms, and a notable proportion can lead to invasive tumors. However, diagnosing is highly complicated error-prone process due their diverse sizes, shapes, subtle variations. In this study, we propose radiomic signature that effectively differentiates between healthy tissue, benign microcalcifications, malignant microcalcifications. Radiomic features were extracted from proprietary dataset, composed 380 136 benign, 242 ROIs. Subsequently, two distinct signatures selected differentiate tissue (detection task) (classification task). Machine learning models, namely Support Vector Machine, Random Forest, XGBoost, employed as classifiers. The shared for both tasks was then used train multi-class model capable simultaneously classifying healthy, A significant overlap discovered the detection classification signatures. performance models promising, with XGBoost exhibiting an AUC-ROC 0.830, 0.856, 0.876 classification, respectively. intrinsic interpretability features, use Mean Score Decrease method introspection, enabled models’ clinical validation. fact, most important GLCM Contrast, FO Minimum Entropy, compared found other studies on breast cancer.

Язык: Английский

Процитировано

11
Artificial Intelligence Assisted Nanogenerator Applications DOI
Shumao Xu,

Farid Manshaii,

Xiao Xiao

и другие.

Journal of Materials Chemistry A, Год журнала: 2024, Номер unknown

Опубликована: Янв. 1, 2024

This review examines the integration of artificial intelligence with nanogenerators to develop self-powered, adaptive systems for applications in robotics, wearables, and environmental monitoring.

Язык: Английский

Процитировано

9
The promise and limitations of artificial intelligence in CTPA-based pulmonary embolism detection DOI Creative Commons
Li Lin, Min Peng, Yi Zou

и другие.

Frontiers in Medicine, Год журнала: 2025, Номер 12

Опубликована: Март 19, 2025

Computed tomography pulmonary angiography (CTPA) is an essential diagnostic tool for identifying embolism (PE). The integration of AI has significantly advanced CTPA-based PE detection, enhancing accuracy and efficiency. This review investigates the growing role in diagnosis using CTPA imaging. examines capabilities algorithms, particularly deep learning models, analyzing images detection. It assesses their sensitivity specificity compared to human radiologists. systems, large datasets complex neural networks, demonstrate remarkable proficiency subtle signs PE, aiding clinicians timely accurate diagnosis. In addition, AI-powered analysis shows promise risk stratification, prognosis prediction, treatment optimization patients. Automated image interpretation quantitative facilitate rapid triage suspected cases, enabling prompt intervention reducing delays. Despite these advancements, several limitations remain, including algorithm bias, interpretability issues, necessity rigorous validation, which hinder widespread adoption clinical practice. Furthermore, integrating into existing healthcare systems requires careful consideration regulatory, ethical, legal implications. conclusion, AI-driven detection presents unprecedented opportunities enhance precision However, addressing associated critical safe effective implementation routine Successful utilization revolutionizing care necessitates close collaboration among researchers, medical professionals, regulatory organizations.

Язык: Английский

Процитировано

1
Introducing an Artificial Neural Network for Virtually Increasing the Sample Size of Bioequivalence Studies DOI Creative Commons
Dimitris Papadopoulos, Vangelis Karalis

Applied Sciences, Год журнала: 2024, Номер 14(7), С. 2970 - 2970

Опубликована: Март 31, 2024

Sample size is a key factor in bioequivalence and clinical trials. An appropriately large sample necessary to gain valuable insights into designated population. However, sizes lead increased human exposure, costs, longer time for completion. In previous study, we introduced the idea of using variational autoencoders (VAEs), type artificial neural network, synthetically create studies. this work, further elaborate on expand it field (BE) A computational methodology was developed, combining Monte Carlo simulations 2 × crossover BE trials with deep learning algorithms, specifically VAEs. Various scenarios, including variability levels, actual size, VAE-generated difference performance between two pharmaceutical products under comparison, were explored. All showed that incorporating AI generative algorithms creating virtual populations has many advantages, as less data can be used achieve similar, even better, results. Overall, work shows how application like VAEs, clinical/bioequivalence studies modern tool significantly reduce trial completion time.

Язык: Английский

Процитировано

3
Novel Computational and Artificial Intelligence Models in Cancer Research DOI Open Access
Li Liu, Fuhai Li, Xiaoming Liu

и другие.

Cancers, Год журнала: 2025, Номер 17(1), С. 116 - 116

Опубликована: Янв. 2, 2025

The ICIBM 2023 marked the 11th annual conference of its kind, with recently becoming official International Association for Intelligent Biology and Medicine (IAIBM), showcasing cutting-edge advancements at intersection computation biomedical research [...]

Язык: Английский

Процитировано

0
Increasing Neural-Based Pedestrian Detectors’ Robustness to Adversarial Patch Attacks Using Anomaly Localization DOI Creative Commons
O. V. Ilina, М. В. Терешонок, Vadim Ziyadinov

и другие.

Journal of Imaging, Год журнала: 2025, Номер 11(1), С. 26 - 26

Опубликована: Янв. 17, 2025

Object detection in images is a fundamental component of many safety-critical systems, such as autonomous driving, video surveillance and robotics. Adversarial patch attacks, being easily implemented the real world, provide effective counteraction to object by state-of-the-art neural-based detectors. It poses serious danger various fields activity. Existing defense methods against attacks are insufficiently effective, which underlines need develop new reliable solutions. In this manuscript, we propose method helps increase robustness neural network systems input adversarial images. The proposed consists Deep Convolutional Neural Network reconstruct benign image from one; Calculating Maximum Error block highlight mismatches between reconstructed images; Localizing Anomalous Fragments extract anomalous regions using Isolation Forest algorithm histograms images' fragments; Clustering Processing group evaluate extracted regions. method, based on anomaly localization, demonstrates high resistance while maintaining quality detection. experimental results show that defending attacks. Using YOLOv3 with defensive for pedestrian INRIAPerson dataset under mAP50 metric reaches 80.97% compared 46.79% without method. research demonstrate promising improvement security.

Язык: Английский

Процитировано

0
An Overview of AI Applications in Wildlife Conservation DOI
Binod Kumar, Oindrilla Ghosh

Advances in environmental engineering and green technologies book series, Год журнала: 2025, Номер unknown, С. 19 - 48

Опубликована: Янв. 10, 2025

The integration of artificial intelligence (AI) into wildlife conservation has revolutionized methodologies for monitoring species, enhancing habitat management, and combating poaching. This chapter examines various AI applications that contribute to the protection preservation biodiversity. Remote sensing technologies, powered by machine learning algorithms, assist in assessing health tracking changes over time. AI-driven image recognition tools enable identification individual animals from camera trap photos, facilitating more accurate population estimates behavioral studies. Moreover, predictive analytics play a crucial role forecasting human-wildlife conflicts informing proactive management strategies. synthesis technologies demonstrates their potential enhance efforts, optimize resource allocation, ultimately foster effective initiatives. ongoing advancement this field promises create innovative solutions some most pressing challenges.

Язык: Английский

Процитировано

0
A comprehensive review of neurotransmitter modulation via artificial intelligence: A new frontier in personalized neurobiochemistry DOI

Jaleh Bagheri Hamzyan Olia,

Arasu Raman, Chou‐Yi Hsu

и другие.

Computers in Biology and Medicine, Год журнала: 2025, Номер 189, С. 109984 - 109984

Опубликована: Март 14, 2025

Язык: Английский

Процитировано

0