e-ISSN:0976-5166
p-ISSN:2231-3850


INDIAN JOURNAL OF COMPUTER SCIENCE AND ENGINEERING

Call for Papers 2020

Apr 2021 - Volume 12, Issue 2
Deadline: 15 Mar 2021
Publication: 20 Apr 2021

Jun 2021 - Volume 12, Issue 3
Deadline: 15 May 2021
Publication: 20 Jun 2021

More

Indexed in

IJCSE Indexed in Scopus

ABSTRACT

Title : BREAST CANCER DETECTION USING MARK RCNN SEGMENTATION AND ENSEMBLE CLASSIFICATION WITH FEATURE EXTRACTION
Authors : Prasath Alias Surendhar S, R.Vasuki
Keywords : Breast cancer, Mammographic mass, Machine learning (ML), pre-processing, Mask RCNN, inception V3, ResNet 152, decision tree, random forest.
Issue Date : Jan-Feb 2021
Abstract :
Breast cancer continues to be the common cancer which causes more decease among women and about more than two million cases is identified every year and according to the record 523,000 deaths are caused per year due to breast cancer. Mammographic mass identification and segmentation are accomplished usually as sequential and distinct tasks, where in previous studies segmentation was often manually performed only on true positive cases. Machine learning (ML) approaches have been grown from manually provided inputs to systematic initializations. The developments in ML techniques have produced independent and more intelligent computer-aided diagnosis (CAD) systems. Moreover, due to the learning ability, ML techniques have been upgraded constantly. Recently, ML techniques are progressive with deeper and varied representation methods, generally termed as deep learning (DL) techniques, and have produced significant impacts on increasing the ability of diagnosing using CAD systems. So this paper proposes the novel architecture in detecting the breast cancer. Here the input database is mammogram image dataset. Initially this image has been resized by pre-processing process, then this pre-processed image has been segmented using Mask RCNN (Region-based convolution neural networks). Then this segmented image has been sent for extracting the feature using inception V3 and ResNet 152 networks. The ensemble classifier of decision tree and random forest has been used for classification and this classified output is detected based on the color variation and tumor size gives the enhanced accuracy in detecting the cancer.
Page(s) : 239-245
ISSN : 0976-5166
Source : Vol. 12, No.1
PDF : Download
DOI : 10.21817/indjcse/2021/v12i1/211201253