Cancernet Classifier for Breast Cancer Classification Using Deep Neural Networks and U-NET segmentation

Cancernet Classifier for Breast Cancer Classification Using Deep Neural Networks and U-NET segmentation

Registration ID: IJNRD_181520

Published ID: IJNRD2206009

DOI: Click Here to Get

Anu Krishnan K R , Dr. N. Satyabalaji

Breast cancer, histopathological images, transfer learning, CNN, VGG-19, UNet

In the present situation accurate breast cancer detection using automated algorithms is one of the most discussing issue. Despite the fact that a lot of effort has been put into addressing this issue, an exact answer has that the majority of existing datasets are unbalanced, which means that the number of occurrences of one class vastly outnumbers those of the others. In this paper, we proposed a framework based on the concept of transfer learning and segmentation to address this issue and focus on histopathological and imbalanced image classification. To increase the overall performance of the system, we will employ the Convolutional Neural Network model with segmentation and supplement it with many state-of-the-art methodologies. The learnt knowledge was applied to the target domain of histopathology pictures using the ImageNet dataset as the source domain

Published Paper   E-Certificate

"Cancernet Classifier for Breast Cancer Classification Using Deep Neural Networks and U-NET segmentation", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.7, Issue 6, page no.75-82, June-2022, Available :https://ijnrd.org/papers/IJNRD2206009.pdf

Volume 7 Issue 6, June-2022

Pages : 75-82

Paper Reg. ID: IJNRD_181520

Published Paper Id: IJNRD2206009

Downloads: 000121129

Research Area: Engineering

Country: Kollam, Kerala, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2206009.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2206009

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Publisher: IJNRD (IJ Publication) Janvi Wave

This work is licensed under a Creative Commons Attribution 4.0 International License and The Open Definition

Call For Paper - Volume 10 | Issue 8 | August 2025

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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (IJNRD) aims to explore advances in research pertaining to applied, theoretical and experimental Technological studies. The goal is to promote scientific information interchange between researchers, developers, engineers, students, and practitioners working in and around the world. IJNRD will provide an opportunity for practitioners and educators of engineering field to exchange research evidence, models of best practice and innovative ideas.

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