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Enhancing the Breast Histopathology Image Analysis for Cancer Detection Using Variational Autoencoder

Author

Listed:
  • Harsh Vardhan Guleria

    (Symbiosis Institute of Technology, Symbiosis International University, Pune 412115, India)

  • Ali Mazhar Luqmani

    (Symbiosis Institute of Technology, Symbiosis International University, Pune 412115, India)

  • Harsh Devendra Kothari

    (Symbiosis Institute of Technology, Symbiosis International University, Pune 412115, India)

  • Priyanshu Phukan

    (Symbiosis Institute of Technology, Symbiosis International University, Pune 412115, India)

  • Shruti Patil

    (Symbiosis Institute of Technology, Symbiosis International University, Pune 412115, India)

  • Preksha Pareek

    (Symbiosis Institute of Technology, Symbiosis International University, Pune 412115, India)

  • Ketan Kotecha

    (Symbiosis Institute of Technology, Symbiosis International University, Pune 412115, India)

  • Ajith Abraham

    (Faculty of Computing and Data Sciences, FLAME University, Lavale, Pune 412115, India)

  • Lubna Abdelkareim Gabralla

    (Department of Computer Science and Information Technology, College of Applied, Nourah Bint Abdulrahman University, Riyadh 11671, Saudi Arabia)

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.

Suggested Citation

  • Harsh Vardhan Guleria & Ali Mazhar Luqmani & Harsh Devendra Kothari & Priyanshu Phukan & Shruti Patil & Preksha Pareek & Ketan Kotecha & Ajith Abraham & Lubna Abdelkareim Gabralla, 2023. "Enhancing the Breast Histopathology Image Analysis for Cancer Detection Using Variational Autoencoder," IJERPH, MDPI, vol. 20(5), pages 1-17, February.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:5:p:4244-:d:1082272
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    References listed on IDEAS

    as
    1. Yang Wu & Lihong Xu, 2021. "Image Generation of Tomato Leaf Disease Identification Based on Adversarial-VAE," Agriculture, MDPI, vol. 11(10), pages 1-18, October.
    2. Jiaxin Li & Zijun Zhou & Jianyu Dong & Ying Fu & Yuan Li & Ze Luan & Xin Peng, 2021. "Predicting breast cancer 5-year survival using machine learning: A systematic review," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-23, April.
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