ArticleInternational journal of environmental research and public health2023
Enhancing the Breast Histopathology Image Analysis for Cancer Detection Using Variational Autoencoder.
Article in International journal of environmental research and public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Bridging traditional and deep learning methods in H&E histological image normalization: a comprehensive review and introducing a novel framework for comparative analyses.Journal of advanced research · 2026Review
- Interpretable multitask model for clinical pathology image prediction and interpretation.NPJ systems biology and applications · 2026Article
- Histopathological image based breast cancer diagnosis using deep learning and bio inspired optimization.Scientific reports · 2025Article
- Hybrid generative adversarial network based on frequency and spatial domain for histopathological image synthesis.BMC bioinformatics · 2025Article
- Advancements in deep learning for early diagnosis of Alzheimer's disease using multimodal neuroimaging: challenges and future directions.Frontiers in neuroinformatics · 2025Review
- Histopathology-based breast cancer prediction using deep learning methods for healthcare applications.Frontiers in oncology · 2024Article
- A Modified LeNet CNN for Breast Cancer Diagnosis in Ultrasound Images.Diagnostics (Basel, Switzerland) · 2023Article
- Automatic Classification of Magnetic Resonance Histology of Peripheral Arterial Chronic Total Occlusions Using a Variational Autoencoder: A Feasibility Study.Diagnostics (Basel, Switzerland) · 2023Article
- The Application of Artificial Intelligence to Cancer Research: A Comprehensive Guide.Technology in cancer research & treatmentReview
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A breast tissue biopsy is performed to identify the nature of a tumour, as it can be either cancerous or benign. The first implementations involved the use of machine learning algorithms. Random Forest and Support Vector Machine (SVM) were used to classify the input histopathological images into whether they were cancerous or non-cancerous. The implementations continued to provide promising results, and then Artificial Neural Networks (ANNs) were applied for this purpose. We propose an approach for reconstructing the images using a Variational Autoencoder (VAE) and the Denoising Variational Autoencoder (DVAE) and then use a Convolutional Neural Network (CNN) model. Afterwards, we predicted whether the input image was cancerous or non-cancerous. Our implementation provides predictions with 73% accuracy, which is greater than the results produced by our custom-built CNN on our dataset. The proposed architecture will prove to be a new field of research and a new area to be explored in the field of computer vision using CNN and Generative Modelling since it incorporates reconstructions of the original input images and provides predictions on them thereafter.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.