Early Breast Cancer Detection Based on Deep Learning: An Ensemble Approach Applied to Mammograms DOI Creative Commons
Youness Khourdifi, Alae El Alami,

Mounia Zaydi

и другие.

BioMedInformatics, Год журнала: 2024, Номер 4(4), С. 2338 - 2373

Опубликована: Дек. 13, 2024

Background: Breast cancer is one of the leading causes death in women, making early detection through mammography crucial for improving survival rates. However, human interpretation mammograms often prone to diagnostic errors. This study addresses challenge accuracy breast by leveraging advanced machine learning techniques. Methods: We propose an extended ensemble deep model that integrates three state-of-the-art convolutional neural network (CNN) architectures: VGG16, DenseNet121, and InceptionV3. The utilizes multi-scale feature extraction enhance both benign malignant masses mammograms. approach evaluated on two benchmark datasets: INbreast CBIS-DDSM. Results: proposed achieved significant performance improvements. On dataset, attained 90.1%, recall 88.3%, F1-score 89.1%. For CBIS-DDSM reached 89.5% 90.2% specificity. method outperformed each individual CNN model, reducing false positives negatives, thereby providing more reliable results. Conclusions: demonstrated strong potential as a decision support tool radiologists, offering accurate earlier cancer. By complementary strengths multiple architectures, this can improve clinical accessibility high-quality screening.

Язык: Английский

Explainable artificial intelligence: A survey of needs, techniques, applications, and future direction DOI
Melkamu Mersha, Khang Nhứt Lâm, Joseph Wood

и другие.

Neurocomputing, Год журнала: 2024, Номер 599, С. 128111 - 128111

Опубликована: Сен. 1, 2024

Язык: Английский

Процитировано

18
AI in diagnostics: Enhancing accuracy and efficiency DOI

Sameer Mohommed Khan

Elsevier eBooks, Год журнала: 2025, Номер unknown, С. 279 - 304

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

1
Artificial Intelligence in Personalized Medicine for Head and Neck Cancer: Optimizing Prescriptions and Treatment Planning DOI
Karthikeyan Elumalai, Sivaneswari Srinivasan

Deleted Journal, Год журнала: 2025, Номер unknown

Опубликована: Март 27, 2025

Язык: Английский

Процитировано

0
Dynamic Surgical Prioritization: A Machine Learning and XAI-Based Strategy DOI Creative Commons
Fabián Silva-Aravena, Jenny Morales, Manoj Jayabalan

и другие.

Technologies, Год журнала: 2025, Номер 13(2), С. 72 - 72

Опубликована: Фев. 8, 2025

Surgical waiting lists present significant challenges to healthcare systems, particularly in resource-constrained settings where equitable prioritization and efficient resource allocation are critical. We aim address these issues by developing a novel, dynamic, interpretable framework for prioritizing surgical patients. Our methodology integrates machine learning (ML), stochastic simulations, explainable AI (XAI) capture the temporal evolution of dynamic scores, qp(t), while ensuring transparency decision making. Specifically, we employ Light Gradient Boosting Machine (LightGBM) predictive modeling, simulations account variables competitive interactions, SHapley Additive Explanations (SHAPs) interpret model outputs at both global patient-specific levels. hybrid approach demonstrates strong performance using dataset 205 patients from an otorhinolaryngology (ENT) unit high-complexity hospital Chile. The LightGBM achieved mean squared error (MSE) 0.00018 coefficient determination (R2) value 0.96282, underscoring its high accuracy estimating qp(t). Stochastic effectively captured changes, illustrating that Patient 1’s qp(t) increased 0.50 (at t=0) 1.026 t=10) due growth such as severity urgency. SHAP analyses identified (Sever) most influential variable, contributing substantially non-clinical factors, capacity participate family activities (Lfam), exerted moderating influence. Additionally, our achieves reduction times up 26%, demonstrating effectiveness optimizing prioritization. Finally, strategy combines adaptability interpretability, transparent aligns with evolving patient needs constraints.

Язык: Английский

Процитировано

0
Explainable Artificial Intelligence: A Survey of the Need, Techniques, Applications, and Future Direction DOI
Melkamu Mersha,

Khang Lamb,

Joseph Wood

и другие.

Опубликована: Янв. 1, 2024

Artificial intelligence models encounter significant challenges due to their black-box nature, particularly in safety-critical domains such as healthcare, finance, autonomous vehicles, and justice. Explainable Intelligence (XAI) addresses these by providing explanations for how make decisions predictions, ensuring transparency, accountability, fairness. Existing studies have examined the fundamental concepts of XAI, its general principles, scope XAI techniques. However, there remains a gap literature are no comprehensive reviews that delve into detailed mathematical representations, design methodologies models, other associated aspects. This paper provides review encompassing common terminologies definitions, need beneficiaries taxonomy methods, application methods different areas. The survey is aimed at researchers, practitioners, AI model developers, who interested enhancing trustworthiness, fairness models.

Язык: Английский

Процитировано

2
Unravelling AI and Machine Learning Essentials in Alzheimer's Research DOI

V. Saravanan,

Ruth Ramya Kalangi,

T. Saravanan

и другие.

Advances in medical technologies and clinical practice book series, Год журнала: 2024, Номер unknown, С. 147 - 159

Опубликована: Июнь 28, 2024

Artificial intelligence (AI) and system mastering (ML) have received a good-sized interest in Alzheimer's studies due to their capability enhance prognosis treatment. But comprehensive know-how of these technologies software remains lacking. This review objectives resolve the essentials AI ML studies, highlighting capacity effect on sickness development control. The results outline modern-day nation use research challenges implementation, providing foundation for additional improvements this subject. field has been greatly impacted by way fast improvement artificial studying techniques. With growing quantity records being generated discipline need more accurate predictions remedies, come be crucial gear unraveling complexities disease.

Язык: Английский

Процитировано

1
Demystifying Machine Learning by Unraveling Interpretability DOI
Anudeep Kotagiri

Advances in systems analysis, software engineering, and high performance computing book series, Год журнала: 2024, Номер unknown, С. 145 - 156

Опубликована: Апрель 29, 2024

In this chapter, the authors embark on a journey to unveil complexities of machine learning by focusing crucial aspect interpretability. As algorithms become increasingly sophisticated and pervasive across industries, understanding how these models make decisions is essential for trust, accountability, ethical considerations. They delve into various techniques methodologies aimed at unraveling black box learning, shedding light arrive their predictions classifications. From explainable AI approaches model-agnostic techniques, they explore practical strategies interpreting explaining models. Through real-world examples case studies, illustrate importance interpretability in ensuring transparency, fairness, compliance decision-making processes. Whether you're data scientist, researcher, or business leader, chapter serves as guide navigating complex landscape unlocking true potential technologies.

Язык: Английский

Процитировано

0
Research challenges and future work directions in smart healthcare using IoT and machine learning DOI
Sachin Minocha,

Keinisha Joshi,

Akshita Sharma

и другие.

Advances in computers, Год журнала: 2024, Номер unknown

Опубликована: Янв. 1, 2024

Язык: Английский

Процитировано

0
Unravelling Alzheimer's DOI

G. P. Suja

Advances in medical technologies and clinical practice book series, Год журнала: 2024, Номер unknown, С. 175 - 189

Опубликована: Июнь 28, 2024

The developing prevalence of Alzheimer's ailment has emerged as a main global health challenge, highlighting the urgent want for accurate and well-timed diagnosis. traditional diagnostic techniques have limitations, to delay in analysis treatment. In response this trouble, advancements artificial intelligence (AI) revolutionized prediction disorder. Through utilizing system studying algorithms, AI potential identify meaningful styles massive statistics sets, making an allowance advanced detection more correct sickness. This gives leap forward control ailment, presenting possibilities early intervention affected person results. chapter summarizes contemporary state research, discussing its programs capability enhancing prediction, long run paving way better knowledge treatment debilitating

Язык: Английский

Процитировано

0
Real-World Impact DOI

Kireet Muppavaram,

Amit Gangopadhyay,

Sudhir Ramadass

и другие.

Advances in medical technologies and clinical practice book series, Год журнала: 2024, Номер unknown, С. 237 - 249

Опубликована: Июнь 28, 2024

The sector of artificial intelligence (AI) has shown fantastic capacity in advancing the studies and care Alzheimer's disorder (AD), a degenerative neurological that impacts tens millions globally. In recent years, several case achievement testimonies have emerged spotlight actual-international impact AI ad research care. This summary highlights impactful fulfillment stories, providing evidence how AI-driven processes improved diagnosis, prediction, management adverts. Moreover, it discusses benefits using inside improvement customized treatment strategies for early detection prevention advert through AI-based equipment. concludes by emphasizing important position collaboration between specialists, clinicians, researchers riding further improvements ultimately, enhancing consequences individuals dwelling with AD.

Язык: Английский

Процитировано

0