An Early Breast Cancer Detection System Using Recurrent Neural Network (RNN) with Animal Migration Optimization (AMO) Based Classification Method

S. Prakash, K. Sangeetha
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引用次数: 2

Abstract

Breast cancer can be detected using early signs of it mammograms and digital mammography. For Computer Aided Detection (CAD), algorithms can be developed using this opportunities. Early detection is assisted by self-test and periodical check-ups and it can enhance the survival chance significantly. Due the need of breast cancer’s early detection and false diagnosis impact on patients, made researchers to investigate Deep Learning (DL) techniques for mammograms. So, it requires a non-invasive cancer detection system, which is highly effective, accurate, fast as well as robust. Proposed work has three steps, (i) Pre-processing, (ii) Segmentation, and (iii) Classification. Firstly, preprocessing stage removing noise from images by using mean and median filtering algorithms are used, while keeping its features intact for better understanding and recognition, then edge detection by using canny edge detector. It uses Gaussian filter for smoothening image. Gaussian smoothening is used for enhancing image analysis process quality, result in blurring of fine-scaled image edges. In the next stage, image representation is changed into something, which makes analyses process as a simple one. Foreground and background subtraction is used for accurate breast image detection in segmentation. After completion of segmentation stage, the remove unwanted image in input image dataset. Finally, a novel RNN forclassifying and detecting breast cancer using Auto Encoder (AE) based RNN for feature extraction by integrating Animal Migration Optimization (AMO) for tuning the parameters of RNN model, then softmax classifier use RNN algorithm. Experimental results are conducted using Mini-Mammographic (MIAS) dataset of breast cancer. The classifiers are measured through measures like precision, recall, f-measure and accuracy.
基于动物迁移优化(AMO)分类方法的递归神经网络早期乳腺癌检测系统
乳腺癌可以通过乳房x光检查和数字乳房x光检查来检测。对于计算机辅助检测(CAD),可以利用这个机会开发算法。自检和定期检查有助于早期发现,可显著提高生存率。由于乳腺癌的早期发现和误诊对患者的影响,促使研究人员研究深度学习(DL)技术用于乳房x光检查。因此,它需要一种高效、准确、快速、稳健的非侵入性癌症检测系统。提议的工作有三个步骤,(i)预处理,(ii)分割,和(iii)分类。首先,在保持图像特征完整的前提下,采用均值滤波和中值滤波算法去除图像中的噪声,以便更好地理解和识别图像,然后采用canny边缘检测器进行边缘检测。采用高斯滤波对图像进行平滑处理。为了提高图像分析过程的质量,采用高斯平滑技术,使精细图像的边缘变得模糊。下一阶段,将图像表示转化为某种形式,使分析过程变得简单。在分割过程中,采用前景和背景相减法对乳房图像进行精确检测。分割阶段完成后,去除输入图像数据集中不需要的图像。最后,利用基于自动编码器(Auto Encoder, AE)的RNN进行特征提取,结合动物迁移优化(Animal Migration Optimization, AMO)对RNN模型参数进行调整,构建了一种新型的用于乳腺癌分类和检测的RNN,然后采用RNN算法对softmax分类器进行分类。实验结果使用mini -乳房x线摄影(MIAS)乳腺癌数据集进行。分类器是通过精度、召回率、f-measure和准确度等指标来衡量的。
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