Unsupervised histopathological sub-image analysis for breast cancer diagnosis using variational autoencoders, clustering, and supervised learning
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Tarih
2024
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Mustansiriyah University College of Engineering
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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.
Açıklama
Anahtar Kelimeler
Breast cancer diagnosis, Clustering, Convolutional neural networks, Feature extraction, Supervised Learning, Unsupervised Learning, Variational Autoencoders
Kaynak
Journal of Engineering and Sustainable Development
WoS Q Değeri
Scopus Q Değeri
Q4
Cilt
28
Sayı
6
Künye
Abdulaal, A. H., Valizadeh, M., Yassin, R. A., Albaker, B. M., Abdulwahhab, A. H., Amirani, M. C., Shah, A. F. M. S. (2024). Unsupervised histopathological sub-image analysis for breast cancer diagnosis using variational autoencoders, clustering, and supervised learning. Journal of Engineering and Sustainable Development, 28(6), 729-744. 10.31272/jeasd.28.6.6
