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primary topic: Divulging Patterns: An Analytical Review for Machine Learning Methodologies for Breast Cancer Detection

Divulging Patterns: An Analytical Review for Machine Learning Methodologies for Breast Cancer Detection
type: AcademicArticle
type: Article
abstract: Breast cancer is a lethal carcinoma impacting a considerable number of women across the globe. While preventive measures are limited, early detection remains the most effective strategy. Accurate classification of breast tumors into benign and malignant categories is important which may help physicians in diagnosing the disease faster. This survey investigates the emerging inclination and approaches in the area of machine learning (ML) for the diagnosis of breast cancer, pointing out the classification techniques based on both segmentation and feature selection. Certain datasets such as the Wisconsin Diagnostic Breast Cancer Dataset (WDBC), Wisconsin Breast Cancer Dataset Original (WBCD), Wisconsin Prognostic Breast Cancer Dataset (WPBC), BreakHis, and others are being evaluated in this study for the demonstration of their influence on the performance of the diagnostic tools and the accuracy of the models such as Support vector machine, Convolutional Neural Networks (CNNs) and ensemble approaches. The main shortcomings or research gaps such as prejudice of datasets, scarcity of generalizability, and interpretation challenges are highlighted. This research emphasizes the importance of the hybrid methodologies, cross-dataset validation, and the engineering of explainable AI to narrow these gaps and enhance the overall clinical acceptance of ML-based detection tools.
date: 2025-10
volume: 16
issue: 15
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ArticleEPrint
is part of: repository
has document: Divulging Patterns: An Analytical Review for Machine Learning Methodologies for Breast Cancer Detection (Texto)
has document: Divulging Patterns: An Analytical Review for Machine Learning Methodologies for Breast Cancer Detection (Otro)
subject: Engineering

Divulging Patterns: An Analytical Review for Machine Learning Methodologies for Breast Cancer Detection (Texto)
type: Document
has file: v16p4316.pdf
has part: v16p4316.pdf

Ivyspring International Publisher
type: Organization

Journal of Cancer
type: Collection

Imran Ashraf
type: Person
given name: Imran
family name: Ashraf

Rubén Calderón Iglesias
type: Person
given name: Rubén
family name: Calderón Iglesias

Shoaib Hassan
type: Person
given name: Shoaib
family name: Hassan

Muhammad Umair
type: Person
given name: Muhammad
family name: Umair

Alveena Saleem
type: Person
given name: Alveena
family name: Saleem

Muhammad Zubair
type: Person
given name: Muhammad
family name: Zubair

Muhammad Tahir Naseem
type: Person
given name: Muhammad Tahir
family name: Naseem

Silvia Aparicio Obregón
type: Person
given name: Silvia
family name: Aparicio Obregón

Divulging Patterns: An Analytical Review for Machine Learning Methodologies for Breast Cancer Detection (Otro)
type: Document
is version of: Divulging Patterns: An Analytical Review for Machine Learning Methodologies for Breast Cancer Detection (Texto)
is volatile version of: Divulging Patterns: An Analytical Review for Machine Learning Methodologies for Breast Cancer Detection (Texto)
is index codes version of: Divulging Patterns: An Analytical Review for Machine Learning Methodologies for Breast Cancer Detection (Texto)
has file: indexcodes.txt
has part: indexcodes.txt

HTML Summary of #17863 Divulging Patterns: An Analytical Review for Machine Learning Methodologies for Breast Cancer Detection
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primary topic: Divulging Patterns: An Analytical Review for Machine Learning Methodologies for Breast Cancer Detection

Engineering
type: Concept
preferred label: Ingeniería @es