UGC Approved Journal no 63975

ISSN: 2349-5162 | ESTD Year : 2014
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Published in:

Volume 9 Issue 4
April-2022
eISSN: 2349-5162

UGC and ISSN approved 7.95 impact factor UGC Approved Journal no 63975

7.95 impact factor calculated by Google scholar

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Published Paper ID:
JETIR2204365


Registration ID:
400430

Page Number

d513-d523

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Title

Deep Segmentation and Classification for Breast Cancer Detection in Mammogram Images

Abstract

Breast cancer is one of the most serious diseases, with the highest occurrence worldwide, where early identification and diagnosis, obtained by imaging techniques such as mammography, play a vital role in preventing the disease. For mammography diagnostics, radiologists have a high false positive rate and an accuracy of roughly 82 percent. Deep learning (DL) approaches have demonstrated promising results in developing computer-aided diagnostic (CAD) systems for early identification of breast cancer.Using mammogram images, this research develops a new deep segmentation with residual network (DS-RN) based breast cancer diagnosis model. Pre-processing, Faster Region based Convolution Neural Network (R-CNN) (Faster R-CNN), and Inception v2 model based segmentation, feature extraction, and classification are all part of the described DS-RN model. Random forest (RF) classifier model is used to classify mammogram images. To ensure that the provided model performs better on the Mini-MIAS dataset, a rigorous simulation process is used. The DS-RN model achieved maximum classification performance with sensitivity, specificity, accuracy, and F-Measure of 96.36%, 100 %, 97.75%, and 98.15%, respectively, according to the collected experimental values.

Key Words

Breast cancer, Classification, Segmentation, Feature extraction, Mammogram

Cite This Article

"Deep Segmentation and Classification for Breast Cancer Detection in Mammogram Images", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 4, page no.d513-d523, April-2022, Available :http://www.jetir.org/papers/JETIR2204365.pdf

ISSN


2349-5162 | Impact Factor 7.95 Calculate by Google Scholar

An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 7.95 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator

Cite This Article

"Deep Segmentation and Classification for Breast Cancer Detection in Mammogram Images", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 4, page no. ppd513-d523, April-2022, Available at : http://www.jetir.org/papers/JETIR2204365.pdf

Publication Details

Published Paper ID: JETIR2204365
Registration ID: 400430
Published In: Volume 9 | Issue 4 | Year April-2022
DOI (Digital Object Identifier):
Page No: d513-d523
Country: -, -, India .
Area: Engineering
ISSN Number: 2349-5162
Publisher: IJ Publication


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