Breast Cancer Mass Classification Using Machine Learning, Binary-Coded Genetic Algorithms and an Ensemble of Deep Transfer Learning

The diagnosis of breast cancer (BC) as early as possible is crucial for increasing the survival rate. Mammography enables finding the breast tissue changes years before they could develop into cancer symptoms. In this study, machine learning methods for BC mass pathology classification have been investigated using the radiologists’ mass annotations on the screen-film mammograms of the Breast Cancer Digital Repository (BCDR). The performances of precomputed features in the BCDR and discrete wavelet transform followed by Radon transform have been investigated by using four sequential feature selections and three genetic algorithms. Feature fusion from craniocaudal and mediolateral oblique views was shown to increase the performance of the classifier. Mass classification has been implemented by deep transfer learning (DTL) using the weights of ResNet50, NASNetLarge and Xception networks. An ensemble of DTL (EDTL) was shown to have higher classification performance than the DTL models. The proposed EDTL has area under the receiver operating curve (AUC) scores of 0.8843 and 0.9089 for mass classification on the region of interest (ROI) and ROI union datasets, respectively. The proposed EDTL has the highest BC mass classification AUC score on the BCDR to date and may be useful for other datasets.

[1]  Adel Said Elmaghraby,et al.  An integrated framework for breast mass classification and diagnosis using stacked ensemble of residual neural networks , 2022, Scientific Reports.

[2]  Yao Lu,et al.  Can a Computer-Aided Mass Diagnosis Model Based on Perceptive Features Learned From Quantitative Mammography Radiology Reports Improve Junior Radiologists’ Diagnosis Performance? An Observer Study , 2021, Frontiers in Oncology.

[3]  Aimon Rahman,et al.  Automated detection and grading of Invasive Ductal Carcinoma breast cancer using ensemble of deep learning models , 2021, Comput. Biol. Medicine.

[4]  Surbhi Gupta,et al.  Computational Prediction of Cervical Cancer Diagnosis Using Ensemble-Based Classification Algorithm , 2021, Comput. J..

[5]  Amin Rezaeipanah,et al.  Breast Cancer Diagnosis Using Multi-Stage Weight Adjustment In The MLP Neural Network , 2020, Comput. J..

[6]  V. Krishnaswamy,et al.  The shape of breast cancer , 2020, Breast Cancer Research and Treatment.

[7]  Jinbo Bi,et al.  Residual Deep Learning System for Mass Segmentation and Classification in Mammography , 2019, BCB.

[8]  Wilbert G. Aguilar,et al.  Transfer Learning in Breast Mammogram Abnormalities Classification With Mobilenet and Nasnet , 2019, 2019 International Conference on Systems, Signals and Image Processing (IWSSIP).

[9]  Reyer Zwiggelaar,et al.  Automated Mammogram Analysis with a Deep Learning Pipeline , 2019, 1907.11953.

[10]  Nan Wu,et al.  Deep Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening , 2019, IEEE Transactions on Medical Imaging.

[11]  Johnatan Carvalho Souza,et al.  Diagnosis of breast tissue in mammography images based local feature descriptors , 2018, Multimedia Tools and Applications.

[12]  Daniel L Rubin,et al.  A curated mammography data set for use in computer-aided detection and diagnosis research , 2017, Scientific Data.

[13]  Li Shen,et al.  End-to-end Training for Whole Image Breast Cancer Diagnosis using An All Convolutional Design , 2017, ArXiv.

[14]  Javad Haddadnia,et al.  Evaluation of a New Ensemble Learning Framework for Mass Classification in Mammograms , 2017, Clinical breast cancer.

[15]  Anselmo Cardoso de Paiva,et al.  Texture analysis of masses malignant in mammograms images using a combined approach of diversity index and local binary patterns distribution , 2016, Expert Syst. Appl..

[16]  Daniel Lévy,et al.  Breast Mass Classification from Mammograms using Deep Convolutional Neural Networks , 2016, ArXiv.

[17]  Alireza Rowhanimanesh,et al.  Iranian Journal of Basic Medical Sciences , 2022 .

[18]  Miguel Ángel Guevara-López,et al.  Representation learning for mammography mass lesion classification with convolutional neural networks , 2016, Comput. Methods Programs Biomed..

[19]  Miguel Ángel Guevara-López,et al.  Convolutional neural networks for mammography mass lesion classification , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[20]  Shohreh Kasaei,et al.  Benign and malignant breast tumors classification based on region growing and CNN segmentation , 2015, Expert Syst. Appl..

[21]  Ahmet Sertbas,et al.  Computer‐aided classification of breast masses in mammogram images based on spherical wavelet transform and support vector machines , 2015, Expert Syst. J. Knowl. Eng..

[22]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[23]  Yılmaz Kaya,et al.  A new intelligent classifier for breast cancer diagnosis based on a rough set and extreme learning machine: RS + ELM , 2013 .

[24]  Stephen M. Moore,et al.  The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository , 2013, Journal of Digital Imaging.

[25]  Miguel Ángel Guevara-López,et al.  An evaluation of image descriptors combined with clinical data for breast cancer diagnosis , 2013, International Journal of Computer Assisted Radiology and Surgery.

[26]  Miguel Ángel Guevara-López,et al.  Discovering Mammography-based Machine Learning Classifiers for Breast Cancer Diagnosis , 2012, Journal of Medical Systems.

[27]  Adam Lipowski,et al.  Roulette-wheel selection via stochastic acceptance , 2011, ArXiv.

[28]  Nedim Tutkun,et al.  Parameter estimation in mathematical models using the real coded genetic algorithms , 2009, Expert Syst. Appl..

[29]  Yosvany López,et al.  Breast Cancer Diagnosis Based on a Suitable Combination of Deformable Models and Artificial Neural Networks Techniques , 2007, CIARP.

[30]  L. Breiman.  Random Forests , 2001, Encyclopedia of Machine Learning and Data Mining.

[31]  Anil K. Jain,et al.  Feature Selection: Evaluation, Application, and Small Sample Performance , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[32]  Josef Kittler,et al.  Floating search methods in feature selection , 1994, Pattern Recognit. Lett..

[33]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .

[34]  David E. Goldberg,et al.  Genetic algorithms and Machine Learning , 1988, Machine Learning.

[35]  B. Matthews.  Comparison of the predicted and observed secondary structure of T4 phage lysozyme. , 1975, Biochimica et biophysica acta.

[36]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[37]  A. Wayne Whitney,et al.  A Direct Method of Nonparametric Measurement Selection , 1971, IEEE Transactions on Computers.

[38]  Aura Conci,et al.  Transfer Learning and Fine Tuning in Breast Mammogram Abnormalities Classification on CBIS-DDSM Database , 2020 .

[39]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[40]  David E. Goldberg,et al.  Genetic Algorithms, Tournament Selection, and the Effects of Noise , 1995, Complex Syst..

[41]  J. Radon.  On the determination of functions from their integral values along certain manifolds , 1986, IEEE Transactions on Medical Imaging.

[42]  Thomas Marill,et al.  On the effectiveness of receptors in recognition systems , 1963, IEEE Trans. Inf. Theory.