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

Harsh Vardhan Guleria, Ali Mazhar Luqmani, Harsh Devendra Kothari, Priyanshu Phukan, Shruti Patil (), Preksha Pareek (), Ketan Kotecha, Ajith Abraham and Lubna Abdelkareim Gabralla
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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

IJERPH, 2023, vol. 20, issue 5, 1-17

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.

Keywords: variational autoencoder; histopathology image; deep learning (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
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