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
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Summary: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 followed by a complete description of the architecture and workings of the variational Autoencoder (VAE) used for unsupervised Learning. The encoder network maps the input features to lower dimensions, capturing the most essential information. VAE learns a compressed representation of sub-images, facilitating a more profound understanding of underlying patterns and structures. For this reason, we then employ k-means clustering on the encoded representation to find naturally occurring clusters in our data set comprising a histopathological image. Every single sub-image is later fed into the VGG19-SVM model for classification purposes. During magnification at 100x, this model has attained a fantastic accuracy rate of 98.56%. Combining unsupervised analysis with VAE/k-means clustering and supervised classification with VGG19/SVM can integrate information from both methods, thereby improving the accuracy and robustness of such a task as sub-image classification in breast cancer histopathology.
ISSN:2520-0917
2520-0925
DOI:10.31272/jeasd.28.6.6