Hybrid Feature Extraction for Breast Cancer Classification Using the Ensemble Residual VGG16 Deep Learning Model

Current Bioinformatics, Journal Year: 2024, Volume and Issue: 20(2), P. 149 - 163

Published: Oct. 30, 2024

Introduction: Breast Cancer (BC) is a significant cause of high mortality amongst women globally and probably will remain disease posing challenges about its detectability. Advancements in medical imaging technology have improved the accuracy efficiency breast cancer classification. However, tumor features' complexity data variability still pose challenges. Method: This study proposes Ensemble Residual-VGG-16 model as novel combination Deep Residual Network (DRN) VGG-16 architecture. purposely engineered with maximal precision for task diagnosis based on mammography images. We assessed performance by accuracy, recall, precision, F1-Score. All these metrics indicated this model. The diagnostic residual-VGG16 performed exceptionally well an 99.6%, 99.4%, recall 99.7%, F1 score 98.6%, Mean Intersection over Union (MIoU) 99.8% MIAS datasets. Result: Similarly, INBreast dataset achieved 93.8%, 94.2%, 94.5%, F1-score 93.4%. Conclusion: proposed advancement diagnosis, potential automated grading.

Language: Английский

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DeepOvaNet: A Comprehensive Deep Learning Framework for Predicting and Diagnosing Ovarian Cancer in Women Across Menopausal Transitions

Published: Jan. 11, 2024

Ovarian cancer is a type of that begins in the ovaries, female reproductive organ produces eggs. It fifth most common cause cancer-related death among women. more commonly diagnosed women who have gone through menopause, typically around age 50 years or older, is, across menopausal transition. This study aimed to evaluate effectiveness convolutional neural network (CNN) models detecting ovarian by examining histopathological images. The evaluation performance 18 CNN differentiating between malignant and non-cancerous histological pictures involved executing each model independently 20 times. was assessed employing several metrics derived from confusion matrix, including accuracy (Acc.), sensitivity (Sen.), specificity (Spec.), Precision (Prec.)., F1 score, false- positive rate (FPR), Matthews Correlation Coefficient (MCC), Kappa, Computational time. darknet19 had superior compared all other models, with an average 99.79%, minimum 98.95%, maximum 100%. Additionally, matrix exhibited following mean values: (Sens.) 99.73% (Spec.) 99.84% precision (Prec.) 99.84%, false (FPR) 0.15%, score correlation coefficient (MCC) 99.58%, Kappa computation time 9.58 seconds. In future, deep learning may be employed improve identification subgroups.

Language: Английский

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Artificial Intelligence Trends and Tools for Improving Women’s Health

Journal of Women’s Health Care and Management, Journal Year: 2024, Volume and Issue: 5(2)

Published: April 3, 2024

For decades, women's health has faced significant challenges, including underrepresentation in research, limited access to specialized care, and a persistent gender gap diagnosis treatment.However, wave of innovation powered by artificial intelligence (AI) is poised revolutionize the landscape, offering personalized solutions improved healthcare experiences for women across all phases life.This in-depth exploration delves into evolving landscape AI health.This review highlights prominent trends, showcases innovative tools startups driving positive change, discusses potential impact on various aspects well-being.From care early disease detection mental support information, promises transform experiences.

Language: Английский

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Selective feature-based ovarian cancer prediction using MobileNet and explainable AI to manage women healthcare

Multimedia Tools and Applications, Journal Year: 2024, Volume and Issue: unknown

Published: May 13, 2024

Language: Английский

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Comprehensive analysis of artificial intelligence techniques for gynaecological cancer: symptoms identification, prognosis and prediction

Artificial Intelligence Review, Journal Year: 2024, Volume and Issue: 57(8)

Published: July 29, 2024

Abstract Gynaecological cancers encompass a spectrum of malignancies affecting the female reproductive system, comprising cervix, uterus, ovaries, vulva, vagina, and fallopian tubes. The significant health threat posed by these worldwide highlight crucial need for techniques early detection prediction gynaecological cancers. Preferred reporting items systematic reviews Meta-Analysis guidelines are used to select articles published from 2013 up 2023 on Web Science, Scopus, Google Scholar, PubMed, Excerpta Medical Database, AI technique Based study different cancer, results also compared using various quality parameters such as rate, accuracy, sensitivity, specificity, area under curve precision, recall, F1-score. This work highlights impact cancer women belonging age groups regions world. A detailed categorization traditional like physical-radiological, bio-physical bio-chemical detect organizations is presented in study. Besides, this explores methodology researchers which plays role identifying symptoms at earlier stages. paper investigates pivotal years, highlighting periods when highest number research published. challenges faced while performing AI-based highlighted work. features representations Magnetic Resonance Imaging (MRI), ultrasound, pap smear, pathological, etc., proficient algorithms explored. comprehensive review contributes understanding improving prognosis cancers, provides insights future directions clinical applications. has potential substantially reduce mortality rates linked enabling identification, individualised risk assessment, improved treatment techniques. would ultimately improve patient outcomes raise standard healthcare all individuals.

Language: Английский

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