Detecting Breast Cancer in X-RAY images using image segmentation algorithm and neural networks

Rasha Talib Gdeeb

Abstract


Breast cancer becomes is a nightmare threating woman all over the world, so, all the studies are trying for early detection of it to increase healing of it, it can save 30 percent from infected women which is a big percentage. Dangerous of breast cancer comes from the fact that all the women do not know about it until they have a mammogram image for the breast. It can be detected personally in late stages. That means it is important to make a medical examination periodically to investigate the presence of any cancerous lumps in breast tissue or underarm which can be an indicator for the existence of the tumour. Mammogram rays are an X-RAY applied on the breast which can used to find any problems in the breast like tumor blocks in breast, pain, secretions from nipples. Mammogram rays can detect breast cancer early and decrease the death cases. mammogram imaging starts in 40 age and must done every 3 years to assure the not infection of it. In cases of Genetic disease history, it is important to take the mammogram imaging before 40 age in the state of early tumor detection so it increases the recovery in early stages. This work is a study to create a method to estimate the breast cancer situation in X-RAY images to select an automatic medical solution which passes in three stages, primary aiding, chemical aiding, and eradication.

 


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S,Nanglia, M.Ahmad, Khan, F.Jhanjhi NZ,"An enhanced predictive heterogeneous ensemble model for breast cancer prediction". Biomed Signal Process Control. Vol.218,pp. 1314-1320 ,2022,

M. Swain, S. Kisan, “Hybridized Machine Learning based Fractal Analysis Techniques for Breast Cancer Classification", International Journal of Advanced Computer Vol.1,No2, pp 865,2020

S. Saeed, N. Zaman, "Optimized Breast Cancer Premature Detection Method With Computational Segmentation: A Systematic Review Mapping", PP.28 2022

X. Zhang,Y. Zhang,Q. Zhang, Y.Ren, Qiu T," Extracting comprehensive clinical information for breast cancer using deep learning methods". Int J Med Inform.Vol.5,No.2,pp 62 - 75. (2019)

S. Nanglia, M. Ahmad, F. Ali Khan, N.Z. Jhanjhi," An enhanced Predictive heterogeneous ensemble model for breast cancer prediction", Biomedical Signal Processing and Control ,Vol.132,No. 2022.

A. Mechelli, S. Vieira, "Machine Learning: Methods and Applications to Brain Disorders", ElsevierNo.28,pp.343-370, 2019.

A. Akinyelu, F. Zaccagna ,T. James. , M. Castelli ," Brain Tumor Diagnosis Using Machine Learning, Convolutional Neural Networks, Capsule Neural Networks and Vision Transformers", Applied to MRI: A Survey, J. Imaging,Vol.8,No.8,pp.205, 2022.

W. Al-Dhabyani, M.Gomaa, H. Khaled," Deep Learning Approaches for Data Augmentation and Classification of Breast Masses using Ultrasound Images", Vol. 109, pp. 85-90,2019

Y.Hao, S.Qiao, L. Zhang, T. Xu, Y. Bai," Breast Cancer Histopathological Image Recognition Based on Low Dimensional' Three-Channel Features,Vol.11, 2021

M. Hoon Yap, G. Pons, J.Marti, "Automated Breast Ultrasound Lesions Detection Using Convolutional NeuralNetworks",VOL. 22, NO. 4, pp.1218,2017..

G. TM, S.Abbas,S. Munir, M.Khan MA,M. Ahmad," Alzheimer's disease detection empowered with transfer learning". Comput Mater Contin.Vol.70,No,3, 2021

A. Saber, M.Sakr, O.Seida , A.Keshk , H.Chen." A novel deep-learning model for automatic detection and classification of breast cancer using the transfer-learning technique". IEEE Access.Vol.1,No.1,pp.99 2021.

V.Azevedo, C.Silva , I.Dutra . 'Quantum transfer learning for breast cancer detection. Quantum', Mach Intell.Vol.4,No.5,pp. 2594 (2022)

K.Dewangan, D.Dewangan ,S. Sahu, R.Janghel." Breast cancer diagnosis in an early stage using novel deep learning with hybrid optimization technique". Multimed Tools App. Vol.81, pp.13935–13960 ,2022

A.Rasool , C.Bunterngchit , L. Tiejian , M.RuhulIslam , Q.Qiang , Qingshan Jiang," Improved Machine Learning-Based Predictive Models for Breast Cancer Diagnosis" ,Vol. 19,No.(6),pp.3211. 2021

L. RH, B.Kujabi , C.Chuang, C.Lin, C.Chiu." Application of deep learning to construct breast cancer diagnosis model". Appl Sci.Vol.12,No.4, (2022)

M.Alruwaili, W.Gouda. "Automated breast cancer detection models based on transfer learning. Sensors".Vol.17,No.7,pp.1238, (2022)

M.Alshammari,A. Almuhanna, J.Alhiyafi ," Mammography image-based diagnosis of breast cancer using machine learning: a pilot study". Sensors.Vol,1,No.22.pp.203,(2021)

H. Soni, D. Sankhe, "Image Restoration using Adaptive Median Filtering", International Research Journal of Engineering and Technology (IRJET), Vol. 06 ,No. 10 2019.

M. Kowal,P. Filipczuk, A.Obuchowicz, "Computer-aided diagnosis of breast cancer using Gaussian mixture cytological image segmentation", Vol. 17,No.1642-6037,pp.258-268,2011

A. Ghasemzadeh, S. Sarbazi Azad, E. Esmaeili ," Breast cancer detection based on Gabor-wavelet transform and machine learning methods", International Journal of Machine Learning and Cybernetics Vol. 10, pp.1603–1612 ,2019.

V.Azevedo, C.Silva, I.Dutra," Quantum transfer learning for breast cancer detection". Quantum Mach Intell.Vol.400.No.1818, 2022.

M.Alruwaili ,W. Gouda,"Automated breast cancer detection models based on transfer learning",Sensors.Vol. 22,No,3, pp.876, 2022.




DOI: https://doi.org/10.31449/inf.v47i9.4995

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