Paper Title
Cancernet Classifier for Breast Cancer Classification Using Deep Neural Networks and U-NET segmentation
Article Identifiers
: Authors
Keywords
Breast cancer, histopathological images, transfer learning, CNN, VGG-19, UNet
Abstract
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
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How To Cite (APA)
Anu Krishnan K R & Dr. N. Satyabalaji (June-2022). Cancernet Classifier for Breast Cancer Classification Using Deep Neural Networks and U-NET segmentation. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 7(6), 75-82. https://ijnrd.org/papers/IJNRD2206009.pdf
Issue
Volume 7 Issue 6, June-2022
Pages : 75-82
Other Publication Details
Paper Reg. ID: IJNRD_181520
Published Paper Id: IJNRD2206009
Downloads: 000122256
Research Area: Engineering
Author Type: Indian Author
Country: Kollam, Kerala, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2206009.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2206009
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