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Enhancing the Breast Histopathology Image Analysis for Cancer Detection Using Variational Autoencoder.
Guleria, Harsh Vardhan; Luqmani, Ali Mazhar; Kothari, Harsh Devendra; Phukan, Priyanshu; Patil, Shruti; Pareek, Preksha; Kotecha, Ketan; Abraham, Ajith; Gabralla, Lubna Abdelkareim.
Afiliación
  • Guleria HV; Symbiosis Institute of Technology, Symbiosis International University, Pune 412115, India.
  • Luqmani AM; Symbiosis Institute of Technology, Symbiosis International University, Pune 412115, India.
  • Kothari HD; Symbiosis Institute of Technology, Symbiosis International University, Pune 412115, India.
  • Phukan P; Symbiosis Institute of Technology, Symbiosis International University, Pune 412115, India.
  • Patil S; Symbiosis Institute of Technology, Symbiosis International University, Pune 412115, India.
  • Pareek P; Symbiosis Institute of Technology, Symbiosis International University, Pune 412115, India.
  • Kotecha K; Symbiosis Institute of Technology, Symbiosis International University, Pune 412115, India.
  • Abraham A; Faculty of Computing and Data Sciences, FLAME University, Lavale, Pune 412115, India.
  • Gabralla LA; Department of Computer Science and Information Technology, College of Applied, Nourah Bint Abdulrahman University, Riyadh 11671, Saudi Arabia.
Int J Environ Res Public Health ; 20(5)2023 02 27.
Article en En | MEDLINE | ID: mdl-36901255
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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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Redes Neurales de la Computación / Neoplasias Tipo de estudio: Diagnostic_studies / Prognostic_studies Idioma: En Revista: Int J Environ Res Public Health Año: 2023 Tipo del documento: Article País de afiliación: India Pais de publicación: Suiza
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Redes Neurales de la Computación / Neoplasias Tipo de estudio: Diagnostic_studies / Prognostic_studies Idioma: En Revista: Int J Environ Res Public Health Año: 2023 Tipo del documento: Article País de afiliación: India Pais de publicación: Suiza