Unsupervised Histopathological Sub-Image Analysis for Breast Cancer Diagnosis Using Variational Autoencoders, Clustering, and Supervised Learning

This paper presents an integrated approach to breast cancer diagnosis that combines unsupervised and supervised learning techniques. The method involves using a pre-trained VGG19 model to process sub-images from the BreaKHis dataset, divided into nine parts for comprehensive analysis. This will be f...

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Bibliographic Details
Published inJournal of engineering and sustainable development (Online) Vol. 28; no. 6; pp. 729 - 744
Main Authors Abdulaal, Dr.Alaa Hussein, Valizadeh, Dr.Morteza, Yassin, Riyam Ali, Albaker, Dr.Baraa M., Abdulwahhab, Ali H., Amirani, Dr.Mehdi Chehel, Shah, Dr.A. F. M. Shahen
Format Journal Article
LanguageEnglish
Published Mustansiriyah University/College of Engineering 01.11.2024
Subjects
Breast cancer diagnosis
Clustering
Convolutional Neural Network
Feature extraction
Supervised learning
Unsupervised learning
Online AccessGet full text
ISSN2520-0917
2520-0925
DOI10.31272/jeasd.28.6.6

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