Diagnosing Skin Cancer Using Shearlet Transform Multiresolution Computation DOI Creative Commons
Abdul Razak Mohamed Sikkander, Maheshkumar H. Kolekar,

V. Bagya Lakshmi

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 26, 2024

Abstract Skin cancer diagnosis relies on the accurate analysis of medical images to identify malignant and benign lesions. The Shearlet transform, a powerful mathematical tool for multiresolution analysis, has shown promise in enhancing detection classification skin cancer. This study investigates application transform-based diagnosis. known its ability capture anisotropic features directional information, provides comprehensive representation lesion at multiple scales orientations. We integrate transform with advanced image processing techniques extract discriminative from dermoscopic images. These are then utilized train machine learning classifier, specifically support vector (SVM), distinguish between proposed methodology is evaluated publicly available dataset, results demonstrate significant improvements diagnostic accuracy compared traditional methods. Our approach enhances feature extraction capabilities, leading more reliable precise diagnosis, ultimately contributing better patient outcomes.

Language: Английский

Advancing Cancer Drug Delivery with Nanoparticles: Challenges and Prospects in Mathematical Modeling for In Vivo and In Vitro Systems DOI Open Access
Tozivepi Aaron Munyayi, Anine Crous

Cancers, Journal Year: 2025, Volume and Issue: 17(2), P. 198 - 198

Published: Jan. 9, 2025

Mathematical models are crucial for predicting the behavior of drug conjugate nanoparticles and optimizing delivery systems in cancer therapy. These simulate interactions among nanoparticle properties, tumor characteristics, physiological conditions, including resistance targeting specificity. However, they often rely on assumptions that may not accurately reflect vivo conditions. In vitro studies, while useful, fully capture complexities environment, leading to an overestimation nanoparticle-based therapy effectiveness. Advancements mathematical modeling, supported by preclinical data artificial intelligence, vital refining therapies improving their translation into effective clinical treatments.

Language: Английский

Citations

0
Exploring the therapeutic potentials of cuminaldehyde: a comprehensive review of biological activities, mechanisms, and novel delivery systems DOI
Abhik Paul,

Sai Satyaprakash Mishra,

Avik Maji

et al.

Phytochemistry Reviews, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 22, 2025

Language: Английский

Citations

0
Promoting Transparency in Generative AI DOI
Jerry John Kponyo, Francis Kemausuor, Eric Tutu Tchao

et al.

Oxford University Press eBooks, Journal Year: 2025, Volume and Issue: unknown

Published: April 22, 2025

Abstract The spread of Generative Artificial Intelligence (GAI) across the globe presents both significant opportunities and challenges, particularly within key sectors such as agriculture, energy, healthcare in Africa. While these technologies show promise revolutionizing industries by enhancing efficiency, lack transparency surrounding GAI models raises concerns about accountability, fairness, ethical implications. This chapter explores importance promoting applications African context. By focusing on context, this further aims to contribute global discourse AI transparency, offering insights recommendations that can help bridge gap between technological advancement responsibility vital sectors.

Language: Английский

Citations

0
Computational intelligence techniques for achieving sustainable development goals in female cancer care DOI Creative Commons
Sarad Pawar Naik Bukke, Rajasekhar Komarla Kumarachari,

Eashwar Sai Komarla Rajasekhar

et al.

Discover Sustainability, Journal Year: 2024, Volume and Issue: 5(1)

Published: Nov. 11, 2024

This narrative review explores the intersection of computational intelligence (CI) techniques and Sustainable Development Goals (SDGs) in context female cancer patients. With increasing prevalence among women worldwide, there is a pressing need to integrate advanced methods enhance diagnosis, treatment, management. highlights various CI methods, including artificial intelligence, machine learning data science, examines their contributions achieving specific SDGs like health well-being (SDG 3), gender parity 5), reduced disparity 10). Additionally, considers impact on other relevant SDGs, such as poverty eradication 1), quality education 4), economic growth decent work 8), innovation infrastructure 9), global partnerships 17). The paper discusses current state applications care, identifies challenges, proposes future directions for research practice.

Language: Английский

Citations

1
Diagnosing Skin Cancer Using Shearlet Transform Multiresolution Computation DOI Creative Commons
Abdul Razak Mohamed Sikkander, Maheshkumar H. Kolekar,

V. Bagya Lakshmi

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 26, 2024

Abstract Skin cancer diagnosis relies on the accurate analysis of medical images to identify malignant and benign lesions. The Shearlet transform, a powerful mathematical tool for multiresolution analysis, has shown promise in enhancing detection classification skin cancer. This study investigates application transform-based diagnosis. known its ability capture anisotropic features directional information, provides comprehensive representation lesion at multiple scales orientations. We integrate transform with advanced image processing techniques extract discriminative from dermoscopic images. These are then utilized train machine learning classifier, specifically support vector (SVM), distinguish between proposed methodology is evaluated publicly available dataset, results demonstrate significant improvements diagnostic accuracy compared traditional methods. Our approach enhances feature extraction capabilities, leading more reliable precise diagnosis, ultimately contributing better patient outcomes.

Language: Английский

Citations

0