Automatic Breast Cancer Detection Using Digital Thermal Images

Breast cancer is one of the most common and main reason of death for women all over the world. About one in eight of women is subject to breast cancer over the course of her lifetime. There is no effective method to prevent or know the reasons of growing these cancerous cells, however the number of deaths can be reduced by early detection. Breast cancer detection and classification is one of the most important fields that the researchers are working on. Thermal breast images are considered as an efficient type of screening strategies. The aim of this study is to develop an efficient system to detect breast cancer by using image processing techniques. The proposed system extracts the characteristic features of the breast from the region of interest that is segmented using a novel approach from the thermal input image. Then the image is classified based on these features to normal or abnormal using a neural network classifier. The system is evaluated on a benchmark dataset and a success rate of 96.51% is obtained.

[1]  Mita Nasipuri,et al.  Wavelet based thermogram analysis for breast cancer detection , 2015, 2015 International Symposium on Advanced Computing and Communication (ISACC).

[2]  U. Rajendra Acharya,et al.  Thermography Based Breast Cancer Detection Using Texture Features and Support Vector Machine , 2012, Journal of Medical Systems.

[3]  D. Kennedy,et al.  A Comparative Review of Thermography as a Breast Cancer Screening Technique , 2009, Integrative cancer therapies.

[4]  Doaa M. Shawky,et al.  Employing Spectral Domain Features for Efficient Collaborative Filtering , 2017, ArXiv.

[5]  Doaa M. Shawky and Ahmed F. Seddik.  On the Temporal Effects of Features on the Prediction of Breast Cancer Survivability , 2017 .

[6]  Alireza Khosravi,et al.  Breast Cancer Detection Using BA-BP Based Neural Networks and Efficient Features , 2011, 2011 7th Iranian Conference on Machine Vision and Image Processing.

[7]  Doaa M. Shawky,et al.  An affordable approach for detecting drivers' drowsiness using EEG signal analysis , 2015, 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

[8]  Hossein Pourghassem,et al.  Breast cancer detection using MRF-based probable texture feature and decision-level fusion-based classification using HMM on thermography images , 2016, Pattern Recognit..

[9]  Aura Conci,et al.  A New Database for Breast Research with Infrared Image , 2014 .

[10]  Rüdiger W. Brause,et al.  Medical Analysis and Diagnosis by Neural Networks , 2001, ISMDA.

[11]  Nader Abd El-Rahman Mohamed.  Breast cancer risk deteotion using digital infrared thermal images , 2015 .

[12]  Doaa M. Shawky,et al.  A low-cost screening method for the detection of the carotid artery diseases , 2013, Knowl. Based Syst..

[13]  U. Rajendra Acharya,et al.  Higher order spectra analysis of breast thermograms for the automated identification of breast cancer , 2014, Expert Syst. J. Knowl. Eng..

[14]  Bryan F. Jones,et al.  A reappraisal of the use of infrared thermal image analysis in medicine , 1998, IEEE Transactions on Medical Imaging.

[15]  Doaa M. Shawky,et al.  Logistic regression model for breast cancer automatic diagnosis , 2015, 2015 SAI Intelligent Systems Conference (IntelliSys).

[16]  M. Chtourou,et al.  Breast cancer diagnosis using a hybrid evolutionary neural network classifier , 2010, 18th Mediterranean Conference on Control and Automation, MED'10.

[17]  Doaa Shawky,et al.  Characterizing Focused Attention and Working Memory Using EEG , 2018, Sensors.