Improved Framework for Breast Cancer Detection using Hybrid Feature Extraction Technique and FFNN

H K,Ibrahim Mohamed,W. Jeberson

doi:10.14569/ijarai.2016.050801

Abstract

Breast Cancer early detection using terminologies of image processing is suffered from the less accuracy performance in different automated medical tools. To improve the accuracy, still there are many research studies going on different phases such as segmentation, feature extraction, detection, and classification. The proposed framework is consisting of four main steps such as image preprocessing, image segmentation, feature extraction and finally classification. This paper presenting the hybrid and automated image processing based framework for breast cancer detection. For image preprocessing, both Laplacian and average filtering approach is used for smoothing and noise reduction if any. These operations are performed on 256 x 256 sized gray scale image. Output of preprocessing phase is used at efficient segmentation phase. Algorithm is separately designed for preprocessing step with goal of improving the accuracy. Segmentation method contributed for segmentation is nothing but the improved version of region growing technique. Thus breast image segmentation is done by using proposed modified region growing technique. The modified region growing technique overcoming the limitations of orientation as well as intensity. The next step we proposed is feature extraction, for this framework we have proposed to use combination of different types of features such as texture features, gradient features, 2D-DWT features with higher order statistics (HOS). Such hybrid feature set helps to improve the detection accuracy. For last phase, we proposed to use efficient feed forward neural network (FFNN). The comparative study between existing 2D-DWT feature extraction and proposed HOS-2D-DWT based feature extraction methods is proposed.

Highlights

Cancer is the major threat for human being health and its number of patients increasing word wide due to the global warming, even if there are new therapies and treatments proposed by research doctors, but level of cancer defines the ability of its cure
Our contributions showing that proposed work improved the detection accuracy as compared to existing approach
In [7], authors Padmanabhan, S. et al presented the another approach with goal of improving the diagnostic accuracy of early breast cancer using digital mammograms by adopting the simulation tools such as MATLAB with dataset of Mammographic image analysis society (MIAS)

Summary

Introduction

Cancer is the major threat for human being health and its number of patients increasing word wide due to the global warming, even if there are new therapies and treatments proposed by research doctors, but level of cancer defines the ability of its cure. The estimation of death caused by breast cancer for every year is approximately 40,000 females This estimation is measured by WHO (world health organization). Mammography technology helps to detect the breast cancer before it can happen to individual Still this approach is not completely accurate. The present approaches considering that recording of image is done over the X-ray film and that image is interpreted by the medical expertise Such approaches are highly vulnerable to visual inspection error and human error. The early detection rate is increases based on automated analysis mammogram screening as per the reviews and instigations by different researchers. Another approach is screening mammography which is accurate radiological method currently available for early detection of breast cancer. The detection or segmentation of microcalcification supporting the digital mammogram screening in order divide the clusters as benign or malign [2]

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Conclusion