Preprocessing of Breast Cancer Digital Mammograms for AI-Based Detection and Classification Models

Abstract

Breast cancer detection images are the gold standard in the diagnosis and prognosis of the disease. For early diagnosis, the digital mammogram has become the most preferred screening procedure. Mammography has several artifacts that have a deleterious impact on the detection of breast cancer. As a result, eliminating artifacts and improving image quality is necessary in the Computer Aided Diagnosis (CAD) systems. The accuracy and efficiency of the CAD are improved by giving precise Regions of Interest (ROI). ExtractingROI is difficult, since the existence of pectoral muscles affects the detection of abnormalities. The proposed system aims to enhance image quality, facilitate feature extraction, and improve diagnostic accuracy in medical imaging applications. It demonstrates the integration of various techniques to achieve optimal results for clinical analysis and decision-making. The system consists of four key stages. First, remove the image background and eliminate any unwanted noise. Second, segment pectoral muscle using a seed point selection technique based on image orientation. This step is important to identify and delineate the pectoral region within the image. Third, image enhancement techniques to improve the visual quality of the processed image using the Wiener filter for noise reduction and sharpening. Fourth, histogram equalization and intensity refined using a CLAHE filter to enhance local contrast, followed by an intensity filter to refine the image quality. Two different benchmark datasets DDSM, and breast mammography (tomosynthesis) are used to validate the generality and efficiency of the proposed method. The findings of experimental results on the entire database demonstrate that the overall performance of the proposed method evaluated achieves higher values of mean and STD, which indicates the efficiency of the proposed method. It also achieves an average SSIM of 94%, indicating high image similarity even after processing. Additionally, breast and muscle for the entire database are successfully extracted, removing background, achieving 100%. The correctness percentage is 99.04%, while the completeness achieves is 97.49%.

Keywords

Machine learning / artificial intelligence / image processing / breast cancer / tumors

References

[1] Abd El-MawlaN, BerbarM A, El-FishawyN A, El-RashidyM A. A novel federated learning framework for sustainable and efficient breast cancer classification system (FL-L 2 CNN-BCDet). IEEE Access, 2024, 12: 163110-163130

[2] AgwuO E, AkpabioJ U, DosunmuA. Modeling the downhole density of drilling muds using multigene genetic programming. Upstream Oil and Gas Technology, 2021, 6100030

[3] AhmadF, ZahidM, AsimM. Breast cancer detection in mammograms using deep learning: An overview. Artificial Intelligence in Medicine, 2022, 113102037

[4] AhmadJ, AkramS, JaffarA, AliZ, BhattiS M, AhmadA, RehmanS U. Deep learning empowered breast cancer diagnosis: Advancements in detection and classification. PLOS One, 2024, 197e0304757

[5] Al-AntariM A, Al-MasniM A, ChoiM T, HanS M, KimT S. A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification. International Journal of Medical Informatics, 2018, 117: 44-54

[6] AlarabeyyatA, AlhanahnahM. Breast cancer detection using k-nearest neighbor machine learning algorithm. 2016 9th International Conference on Developments in eSystems Engineering (DeSE), 20163539

[7] Al-BalasM, Al-BalasH, AlAmerZ, Al-TaweelG, GhabbounA, AlB F, EleiwatB. Clinical outcomes of screening and diagnostic mammography in a limited resource healthcare system. BMC Women’s Health, 2024, 24(1): 1-7

[8] AlharbiA, SahuP K, OwaisM, ChatterjeeK. Breast cancer prediction using hybrid machine learning models. Journal of Healthcare Engineering, 20213850146

[9] AncyC A, NairL S. An efficient CAD for detection of tumour in mammograms using SVM. 2017 International Conference on Communication and Signal Processing (ICCSP), 201714311435

[10] AntropovaN, HuynhB Q, GigerM L. A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets. Medical Physics, 2017, 44(10): 5162-5171

[11] AsriH, MousannifH, Al MoatassimH. A Hybrid Data Mining Classifier for Breast Cancer Prediction. International Conference on Advanced Intelligent Systems for Sustainable Development, 2019916

[12] AvcH, KarakayaJ. A novel medical image enhancement algorithm for breast cancer detection on mammography images using machine learning. Diagnostics, 2023, 133348

[13] AyonS I, IslamM M, HossainM R. Coronary artery heart disease prediction: A comparative study of computational intelligence techniques. IETE Journal of Research, 2020120

[14] BarbaD, León-SosaA, LugoP, SuquilloD, TorresF, SurreF, ⋯, CaicedoA. Breast cancer, screening and diagnostic tools: All you need to know. Critical Reviews in Oncology/Hematology, 2021, 157103174

[15] BarzamanK, KaramiJ, ZareiZ, HosseinzadehA, KazemiM H, Moradi-KalbolandiS, ⋯, FarahmandL. Breast cancer: Biology, biomarkers, and treatments. International Immunopharmacology, 2020, 84106535

[16] BeeravoluA R, AzamS, JonkmanM, ShanmugamB, KannoorpattiK, AnwarA. Preprocessing of breast cancer images to create datasets for deep-CNN. IEEE Access, 2021, 9: 33438-33463

[17] BellangerM, BarryK, RanaJ, RegnauxJ P. Cost-effectiveness of lifestyle-related interventions for the primary prevention of breast cancer: A rapid review. Frontiers in Medicine, 2020, 6325

[18] Biospectrum’s Community. Statistical analysis of breast cancer in India [Internet]. c2019 [cited 9 December 2021], 2019

[19] BoraV B, KothariA G, KeskarA G. Robust automatic pectoral muscle segmentation from mammograms using texture gradient and Euclidean distance regression. Journal of Digital Imaging, 2016, 29(1): 115-125

[20] BoudouhS S, BouakkazM. Breast cancer: New mammography dual-view classification approach based on pre-processing and transfer learning techniques. Multimedia Tools and Applications, 2024, 83(8): 24315-24337

[21] International Journal of Advanced Computer Science and Applications, 2016, 74

[22] BREASTCANCER.ORG. HER2 status [Internet]. 2020 [cited 2021 Dec 11]. Available from: https://www.breastcancer.org/symptoms/diagnosis/her2.

[23] CardosoJ S, DominguesI, OliveiraH P. Closed shortest path in the original coordinates with an application to breast cancer. International Journal of Pattern Recognition and Artificial Intelligence, 2015, 29011555002

[24] ChakrabortyS, BhatS S, SinghA. A deep learning approach for detecting and classifying breast can cer in mammograms. Journal of Computational Science, 2020, 40101022

[25] ChenY, ZhangJ, HeJ, ZhangX. Breast cancer detection and diagnosis using deep learning methods: A comprehensive survey. Computational Biology and Chemistry, 2019, 79: 15-23

[26] ChengJ, YuanQ, ZhangX, LiM, LiuY, WeiY. Breast cancer detection from mammogram using a multi-scale convolutional neural network. Neurocomputing, 2020, 393: 137-148

[27] ChhillarI, SinghA. An improved soft voting-based machine learning technique to detect breast cancer utilizing effective feature selection and SMOTE-ENN class balancing. Discover Artificial Intelligence, 2025, 514

[28] CortiC, GiachettiP P, EggermontA M, DelalogeS, CuriglianoG. Therapeutic vaccines for breast cancer: Has the time finally come?. European Journal of Cancer, 2022, 160: 150-174

[29] De RaadK B, van GarderenK A, SmitsM, van der VoortS R, IncekaraF, OeiE H G, StarmansM P. The effect of preprocessing on convolutional neural networks for medical image segmentation. 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), 2021655658

[30] DhungelN, CarneiroG, BradleyA P. A deep learning approach for the analysis of masses in mammograms with minimal user intervention. Medical Image Analysis, 2017, 37: 114-128

[31] DongJ, ZhangJ, WuY, WuW. Multi-modal breast cancer prediction based on deep learning. Computers in Biology and Medicine, 2020, 122103835

[32] DudaR O, HartP E. Use of the Hough transformation to detect lines and curves in pictures. Communications of the ACM, 1972, 15(1): 11-15

[33] ElH E M, YassinN I. Malignant and nonmalignant classification of breast lesions in mammograms using convolutional neural networks. Biomedical Signal Processing and Control, 2021, 70102954

[34] ElhoubyE M, YassinN I. Malignant and nonmalignant classification of breast lesions in mammograms using convolutional neural networks. Biomedical Signal Processing and Control, 2024, 70102954

[35] ElMaraghyA W, DevereauxM W. A systematic review and comprehensive classification of pectoralis major tears. Journal of Shoulder and Elbow Surgery, 2012, 21(3): 412-422

[36] El-MawlaN A, BerbarM A, El-FishawyN A, El-RashidyM A. A novel deep learning approach (Bi-xBcNet-96) considering green AI to discover breast cancer using mammography images. Neural Computing and Applications, 2024123

[37] ElsholtzF H, AsbachP, HaasM, et al.. Introducing theNode Reporting and Data System 1.0 (Node-RADS): A concept for standardized assessment of lymph nodes in cancer. European Radiology, 202119

[38] EremiciI, BorleaA, DumitruC, StoianD. Breast cancer risk factors among women with solid breast lesions. Clinics and Practice, 2024, 14(2): 473-485

[39] FerrellS DJr, AhmadI, NguyenC, et al.. Why is cancer so common a disease in people yet so rare at a cellular level?. Medical Hypotheses, 2020, 144110171

[40] GaillardF, SharmaR, EnglishK, et al.Mammography, 2024

[41] GilbertF J, Pinker-DomenigK. Diagnosis and staging of breast cancer: When and how to use mammography, tomosynthesis, ultrasound, contrast-enhanced mammography, and magnetic resonance imaging. Diseases of the Chest, Breast, Heart and Vessels 2019-2022: Diagnostic and Interventional Imaging, 2019, 9: 155-166

[42] GolaganiP P, MahalakshmiT S, BeebiS K. Supervised learning breast cancer data set analysis in MATLAB using novel SVMclassifier. Machine Intelligence and Soft Computing, 2021255263

[43] GuptaA, KaurM, YadavS, GoelA. Application of hybrid deep learning models for breast cancer detection and prediction. Applied Soft Computing, 2018, 72: 151-160

[44] Guzman-CabreraR, Guzmán-SepúlvedaJ R, Torres-CisnerosM, et al.. Digital image processing technique for breast cancer detection. International Journal of Thermophysics, 2013, 34: 1519-1531

[45] HaqueM R, IslamM M, IqbalH, RezaM S, HasanM K. Performance evaluation of random forests and artificial neural networks for the classification of liver disorder. 2018 International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2), 201815

[46] HasanM K, IslamM M, HashemM M A. Mathematical model development to detect breast cancer using multigene genetic programming. 2016 5th International Conference on Informatics, Electronics and Vision (ICIEV), 2016574579

[47] HeathM, BowyerK, KopansD, MooreR, KegelmeyerW PYaffeM J. The Digital Database for Screening Mammography. Proceedings of the Fifth International Workshop on Digital Mammography, 2001212218

[48] HossainM S, KadirM A, AliM A, GhoshP. Breast cancer detection using optimized deep learning-based methods. Computers in Biology and Medicine, 2020, 120103747

[49] HousseinE H, EmamM M, WaleedA. A comprehensive study ofmammogramanalysis for breast cancer detection using deep learning. Journal of Digital Imaging, 2021, 34(1): 97-109

[50] HuX, GuoY, ZhangX. Breast cancer diagnosis with improved deep convolutional neural networks. Future Generation Computer Systems, 2019, 98: 197-206

[51] IslamM Z, IslamM M, AsrafA. A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images. Informatics in Medicine Unlocked, 2020, 20100412

[52] JavedS G, MajidA, LeeY S. Developing a bioinspired multi-gene genetic programming based intelligent estimator to reduce speckle noise from ultrasound images. Multimedia Tools and Applications, 2018, 77(12): 15657-15675

[53] JhaA M, MahapatraA P K, AbrahamJ, GhoshS. Artificial intelligence — Aprimer for diagnosis and interpretation of breast cancer. International Journal of Trends in OncoScience, 20242736

[54] KaurM, GuptaA, BansalJ, YadavS, GoelA. Breast cancer detection using hybrid machine learning models. Journal of Healthcare Engineering, 20215532972

[55] KavousiS, MaharloueiN, RezvaniA, AliabadH A, VardanjaniH M. Worldwide association of the gender inequality with the incidence and mortality of cervical, ovarian, endometrial, and breast cancers. SSM-Population Health, 2024, 25101613

[56] KhanS, IslamN, JanZ, DinI U, RodriguesJ J C. A novel deep learning based framework for the detection and classification of breast cancer using transfer learning. Pattern Recognition Letters, 2019, 125: 1-6

[57] KhanA, BaigM S, AbdulK, AliN. Automated mammogram classification using deep learning techniques for early detection of breast cancer. Medical & Biological Engineering & Computing, 2021, 59(2): 303-315

[58] KhanZ, SiddiquiM A, MehmoodR, NazirM A. Mammogramanalysis for breast cancer detection using machine learning algorithms: Acomprehensive review. Computational Biology and Chemistry, 2024, 96107381

[59] KimS, LeeY, ParkJ, JangJ, LeeK. A hybrid deep learning-based approach for early diagnosis of breast cancer using mammograms. Computers in Biology and Medicine, 2023, 154106523

[60] KoreshH J D. Impact of the preprocessing steps in deep learning-based image classifications. National Academy Science Letters, 202413

[61] KumarP, GuptaS, DasB C. Saliva as a potential non-invasive liquid biopsy for early and easy diagnosis/prognosis of head and neck cancer. Translational Oncology, 2024, 40101827

[62] LesterS C, HicksD GDiagnostic Pathology: Breast, E-Book, 2021

[63] LiY, AmmariS, BalleyguierC, LassauN, ChouzenouxE. Impact of preprocessing and harmonization methods on the removal of scanner effects in brain MRI radiomic features. Cancers, 2021, 13123000

[64] LiH, ChenD, NailonW H, DaviesM E, LaurensonD I. Dual convolutional neural networks for breast mass segmentation and diagnosis in mammography. IEEE Transactions on Medical Imaging, 2021, 41(1): 3-13

[65] LiH, ChenD, NailonW H, DaviesM E, LaurensonD I. Dual convolutional neural networks for breast mass segmentation and diagnosis in mammography. IEEE Transactions on Medical Imaging, 2021, 41(1): 3-13

[66] LitvinA A, BurkinD A, KropinovA A, ParamzinF N. Radiomics and digital image texture analysis in oncology. Modern Technologies in Medicine, 2021, 13(2): 97-104

[67] LiuX, ZhangY, ChenH, HeH, WangL. A comprehensive study of breast cancer classification using deep convolutional neural networks. Medical Image Analysis, 2023, 82101706

[68] LuoL, WangX, LinY, MaX, TanA, ChanR, ChenH. Deep learning in breast cancer imaging: A decade of progress and future directions. IEEE Reviews in Biomedical Engineering, 2024, 18: 130-151

[69] MaitraI K, NagS, BandyopadhyayS K. Technique for preprocessing of digital mammogram. Computer Methods and Programs in Biomedicine, 2012, 107(2): 175-188

[70] MajeedA R, AwanW A, ulH N, AsgharM A, KhanM J. Retinal fundus image refinement with contrast limited adaptive histogramequalization, noise filtration and intensity adjustment. 2020 IEEE 23rd International Multitopic Conference (INMIC), 202016

[71] MalviaS, BagadiS A, DubeyU S, SaxenaS. Epidemiology of breast cancer in Indian women. AsiaPacific Journal of Clinical Oncology, 2017, 13(4): 289-295

[72] Murcia-GomezD, Rojas-ValenzuelaI, ValenzuelaO. Impact of image preprocessing methods and deep learning models for classifying histopathological breast cancer images. Applied Sciences, 2022, 122211375

[73] NaeemS, AliA, QadriS, KhanM W, TairanN, ShahH, AnamS. Machine-learning based hybrid-feature analysis for liver cancer classification using fused (MR and CT) images. Applied Sciences, 2020, 1093134

[74] NHS Community.Breast cancer in women [Internet], 2018

[75] ObeaguE I, ObeaguG U. Breast cancer: A review of risk factors and diagnosis. Medicine, 2024, 1033e36905

[76] OdatR M, HaddadinS, Al ZoubiB, et al.. 99P Postoperative adjuvant radiotherapy in primary malignant angiosarcoma of the breast patients following mastectomy or breast-conserving surgery: A retrospective study. ESMO Open, 2024, 9(1024): 1-2

[77] OmoteshoQ A, EscamillaA, Pérez-RuizE, et al.. Epigenetic targets to enhance antitumor immune response through the induction of tertiary lymphoid structures. Frontiers in Immunology, 2024, 11348156

[78] PatelS, GuptaS, JoshiM, ShahS. Breast cancer detection using machine learning algorithms: A comprehensive study. Journal of Medical Systems, 2020, 449167

[79] PatelS, KapoorM, KumarA, ThakurR. A survey on advanced techniques in medical image analysis for breast cancer detection. Advances in Artificial Intelligence, 2024, 20243587467

[80] PeateI. The immune system. British Journal of Healthcare Assistants, 2021, 15(10): 492-497

[81] PisanoE D, ZongS, HemmingerB M, DeLucaM, JohnstonR E, MullerK, PizerS M. Contrast limited adaptive histogram equalization image processing to improve the detection of simulated spiculations in dense mammograms. Journal of Digital Imaging, 1998, 11: 193-200

[82] PisanoE D, ZongS, HemmingerB M, DeLucaM, JohnstonR E, MullerK, PizerS M. Contrast limited adaptive histogram equalization image processing to improve the detection of simulated spiculations in dense mammograms. Journal of Digital Imaging, 1998, 11: 193-200

[83] PozoD, MoralesL, MaldonadoD, AguilarJ. A novel methodology to obtain optimal PI controller gains using multi-gene genetic programming for FOPTD systems. 2018 IEEE Third Ecuador Technical Chapters Meeting (ETCM), 201816

[84] PrasadK D, RamadeviR. Analysis and comparison of image enhancement techniques for improving PSNR of liver image by median filtering over Wiener filtering. Cardiometry, 2022, 25: 996-1002

[85] PraveenV, RajendranR, SreedharV. Breast cancer classification using hybrid feature extraction methods. International Journal of Imaging Systems and Technology, 2019, 29(3): 143-151

[86] RahimetoS, DebeleeTG, YohannesD, SchwenkerF. Automatic pectoral muscle removal in mammograms. Evolving Systems, 2021, 12(2): 519-526

[87] RajeshS, ChoudhuryN A, MoulikS. Hepatocellular carcinoma (HCC) liver cancer prediction using machine learning algorithms. 2020 IEEE 17th India Council International Conference (INDICON), 202015

[88] Rajkumar R, Gopalakrishnan S, Praveena K, Venkatesan M, Ramamoorthy K, Hephzipah J J (2024). DARKNET-53 convolutional neural network-based image processing for breast cancer detection. Mesopotamian Journal of Artificial Intelligence in Healthcare: 59–68.

[89] RayS K, MukherjeeS. Breast cancer stem cells as novel biomarkers. Clinica Chimica Acta, 2024117855

[90] International Journal of Advanced Computer Science and Applications, 2012, 32

[91] SaroluH E, ShayeaI, SaoudB, AzmiM H, El-SalehA A, SaadS A, AlnakhliM. Machine learning, IoT and 5G technologies for breast cancer studies: A review. Alexandria Engineering Journal, 2024, 89: 210-223

[92] SechopoulosI, TeuwenJ, MannR. Artificial intelligence for breast cancer detection in mammography and digital breast tomosynthesis: State of the art. Seminars in Cancer Biology, 2021, 72: 214-225

[93] SelvarajS, ThangavelS, PrabhakaranM, SathishT. Impressive predictive model for breast cancer based on machine learning. EAI Endorsed Transactions on Pervasive Health and Technology, 202423

[94] SethyP K, PandeyC, KhanD, RafiqueM, BeheraS K, VijaykumarK, PanigrahiD. A cost-effective computer-vision based breast cancer diagnosis. Journal of Intelligent and Fuzzy Systems, 2021, 41(5): 5253-5263

[95] ShahA, MushtaqA, MandokhailF. A review on breast cancer, risk factors, symptoms and some common treatme. SBK Journal of Basic Sciences and Innovative Research, 2021, 1(1): 34-41

[96] ShaikhK, KrishnanS, ThankiRArtificial intelligence in breast cancer early detection and diagnosis, 2021, Cham. Springer. 209249

[97] ShaikhK, KrishnanS, ThankiR. Breast cancer detection and diagnosis using AI. Artificial Intelligence in Breast Cancer Early Detection and Diagnosis, 20217992

[98] SharmaR, AgarwalR, SinghA. Multi-feature based breast cancer diagnosis using machine learning classifiers. Journal of King Saud University-Computer and Information Sciences, 2021, 33(9): 1037-1047

[99] SharmaP, GuptaS, YadavS, VermaM. Deep learning-based approaches for breast cancer detection and classification: A survey. Journal of Healthcare Engineering, 2023, 20231035437

[100] SharmaPMedical Image Processing Using AI (First Edition), 2024

[101] ShawJ S, LiuL. Automatic breast cancer detection and diagnosis using deep learning techniques. Computer Methods and Programs in Biomedicine, 2020, 186105228

[102] SinghS, YadavS K. Automated breast cancer detection using hybrid machine learning models. Computers, Materials & Continua, 2021, 67(3): 2613-2626

[103] SinghS, JainA, GuptaV. Review on hybrid classification techniques for breast cancer diagnosis. Journal of King Saud University-Computer and Information Sciences, 2023, 35(10): 1208-1217

[104] StordalB, HarvieM, AntoniouM N, BellinghamM, ChanD S, DarbreP, ⋯, EvansD G. Breast cancer risk and prevention in 2024: An overview from the Breast Cancer UKBreast Cancer Prevention Conference. Cancer Medicine, 2024, 1318e70255

[105] TaghanakiS A, LiuY, MilesB, Hamarneh. Geometry-based pectoral muscle segmentation from MLO mammogram views. IEEE Transactions on Biomedical Engineering, 2017, 64(11): 2662-2671

[106] International Journal of Cancer Management, 2024, 171

[107] TandonR, AgrawalS, RathoreN P S, MishraA K, JainS K. A systematic review on deep learning-based automated cancer diagnosis models. Journal of Cellular and Molecular Medicine, 2024, 286e18144

[108] ThakurM, ChoudharyV, GuptaP, BhadauriaH. A hybrid deep learning model for breast cancer detection in mammogramimages. Journal of King Saud University-Computer and Information Sciences, 2020, 32(5): 571-579

[109] TrapaniD, SandovalJ, AliagaP T, AscioneL, Maria Berton GiachettiP P, CuriglianoG, GinsburgO. Screening programs for breast cancer: Toward individualized, risk-adapted strategies of early detection. Breast Cancer Research and Treatment: Innovative Concepts, 20246388

[110] TribuanaD, ArdaA L. Image pre-processing approaches toward better learning performance with CNN. Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 2024, 8(1): 1-9

[111] UdayakumarE, SanthiS, VetrivelanP. An investigation of Bayes algorithm and neural networks for identifying the breast cancer. Indian Journal of Medical and Paediatric Oncology, 2017, 38(3): 340-344

[112] UpadhyayA K, KumarA, ShahabT, et al.. Squamous cell carcinoma of breast metastasising to upper lip. BMJ Case Reports CP, 2024, 173e259653

[113] VakaA R, SoniB, ReddyS. Breast cancer detection by leveraging machine learning. ICT Express, 2020, 6(4): 320-324

[114] VelayuthapandianK, KaruppiahG, VadivelS R S, JosephD R V. Mammogramdata analysis: Trends, challenges, and future directions. Computational Intelligence and Modelling Techniques for Disease Detection in Mammogram Images, 2024138

[115] VermaS, JainP K, ChauhanV S, SaxenaA, VarmaK. Review of machine learning techniques for breast cancer detection and diagnosis. Journal of Ambient Intelligence and Humanized Computing, 2021, 12(2): 1251-1266

[116] VikheP S, ThoolV R. Detection and segmentation of pectoral muscle on MLO-view mammogram using enhancement filter. Journal of Medical Systems, 2017, 41(12): 1-13

[117] WangZ, YuG, KangY, ZhaoY, QuQ. Breast tumor detection in digital mammography based on extreme learning machine. Neurocomputing, 2014, 128: 175-184

[118] WangD, KhoslaA, GargeyaR, IrshadH, BeckA H. Deep learning for identifying metastatic breast cancer. arXiv preprint, 2016

[119] WangQ, ZhangY, LiuH. A novel deep learning method for breast cancer classification. Computational Intelligence and Neuroscience, 20206948285

[120] WangQ, LiJ, ZhangH, LiuX, YangL, XuJ. Breast cancer detection using hybrid deep learning models with optimized features. Journal of Computational Biology and Bioinformatics, 2024, 19(1): 1-10

[121] WaniA K, PrakashA, SenaS, et al.. Unraveling molecular signatures in rare bone tumors and navigating the cancer pathway landscapes for targeted therapeutics. Critical Reviews in Oncology/Hematology, 2024104291

[122] World Health OrganizationCancer Country Profiles 2014, 2018

[123] WuH, YangM, LiuX, ZhuangX. A hybrid method for breast cancer classification in mammograms using deep learning and feature selection. Knowledge-Based Systems, 2020, 192105269

[124] XiaK, YinH. Liver detection algorithm based on an improved deep network combined with edge perception. IEEE Access, 2019, 7: 175135-175142

[125] XiaK, YinHQ P, JiangY, WangS. Liver semantic segmentation algorithm based on improved deep adversarial networks in combination of weighted loss function on abdominal CT images. IEEE Access, 2019, 7: 96349-96358

[126] YeF, DewanjeeS, LiY, JhaN K, ChenZ S, KumarA, ⋯, TangH. Advancements in clinical aspects of targeted therapy and immunotherapy in breast cancer. Molecular Cancer, 2023, 221105

[127] YousefniaS, et al.. Breast cancer, subtypes, risk factors, and treatment. The Palgrave Encyclopedia of Disability, 2024114

[128] ZahoorS, ShoaibU, LaliI U. Breast cancer mammograms classification using deep neural network and entropy-controlled whale optimization algorithm. Diagnostics, 2022, 122557

[129] ZahoorS, ShoaibU, LaliI U. Breast cancer mammograms classification using deep neural network and entropy-controlled whale optimization algorithm. Diagnostics, 2022, 122557

[130] ZhangL, LiuW, ZhangX, LiB. Development of an optimized deep learning framework for early diagnosis of breast cancer using mammograms. Journal of Digital Imaging, 2021, 34(4): 673-685

[131] ZhangJ, WuJ, ZhouX S, ShiF, ShenD. Recent advancements in artificial intelligence for breast cancer: Image augmentation, segmentation, diagnosis, and prognosis approaches. Seminars in Cancer Biology, 2023, 96: 11-25

RIGHTS & PERMISSIONS

Systems Engineering Society of China and Springer-Verlag GmbH Germany

Just Accepted

This article has successfully passed peer review and final editorial review, and will soon enter typesetting, proofreading and other publishing processes. The currently displayed version is the accepted final manuscript. The officially published version will be updated with format, DOI and citation information upon launch. We recommend that you pay attention to subsequent journal notifications and preferentially cite the officially published version. Thank you for your support and cooperation.