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2000
Volume 21, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603
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Abstract

Introduction

Breast cancer is a major cause of mortality among women globally. While mammography remains the gold standard for detection, its interpretation is often limited by radiologist variability and the challenge of differentiating benign and malignant lesions. The study explores the use of Auto-Sklearn, an automated machine learning (AutoML) framework, for breast tumor classification based on mammographic radiomic features.

Methods

244 mammographic images were enhanced using Contrast Limited Adaptive Histogram Equalization (CLAHE) and segmented with Active Contour Method (ACM). Thirty-seven radiomic features, including first-order statistics, Gray-Level Co-occurance Matrix (GLCM) texture and shape features were extracted and standardized. Auto-Sklearn was employed to automate model selection, hyperparameter tuning and ensemble construction. The dataset was divided into 80% training and 20% testing set.

Results

The initial Auto-Sklearn model achieved an 88.71% accuracy on the training set and 55.10% on the testing sets. After the resampling strategy was applied, the accuracy for the training set and testing set increased to 95.26% and 76.16%, respectively. The Receiver Operating Curve and Area Under Curve (ROC-AUC) for the standard and resampling strategy of Auto-Sklearn were 0.660 and 0.840, outperforming conventional models, demonstrating its efficiency in automating radiomic classification tasks.

Discussion

The findings underscore Auto-Sklearn’s ability to automate and enhance tumor classification performance using handcrafted radiomic features. Limitations include dataset size and absence of clinical metadata.

Conclusion

This study highlights the application of Auto-Sklearn as a scalable, automated and clinically relevant tool for breast cancer classification using mammographic radiomics.

© 2025 The Author(s). Published by Bentham Science Publishers.
This is an open access article published under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/legalcode
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2025-07-31
2025-09-19

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References

  1. SungH. FerlayJ. SiegelR.L. LaversanneM. SoerjomataramI. JemalA. BrayF. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.CA Cancer J. Clin.202171320924910.3322/caac.2166033538338
     [Google Scholar]
  2. LohmannP. BousabarahK. HoevelsM. TreuerH. Radiomics in radiation oncology—basics, methods, and limitations.Strahlenther. Onkol.20201961084885510.1007/s00066‑020‑01663‑332647917
     [Google Scholar]
  3. RadziSF KarimMKA SaripanMI Abd RahmanMA OsmanNH DalahEZ Impact of image contrast enhancement on stability of radiomics feature quantification on a 2d mammogram radiograph.IEEE Access812772010.1109/ACCESS.2020.3008927
     [Google Scholar]
  4. LiH. ZhuangS. LiD. ZhaoJ. MaY. Benign and malignant classification of mammogram images based on deep learning.Biomed. Signal Process. Control20195134735410.1016/j.bspc.2019.02.017
     [Google Scholar]
  5. LeeJ.Y. LeeK. SeoB.K. ChoK.R. WooO.H. SongS.E. KimE.K. LeeH.Y. KimJ.S. ChaJ. Radiomic machine learning for predicting prognostic biomarkers and molecular subtypes of breast cancer using tumor heterogeneity and angiogenesis properties on MRI.Eur. Radiol.202232165066010.1007/s00330‑021‑08146‑834226990
     [Google Scholar]
  6. ShengW. XiaS. WangY. YanL. KeS. MellisaE. GongF. ZhengY. TangT. Invasive ductal breast cancer molecular subtype prediction by MRI radiomic and clinical features based on machine learning.Front. Oncol.20221296460510.3389/fonc.2022.96460536172153
     [Google Scholar]
  7. PerumalK. MahendranR.K. Ahmad KhanA. KadryS. Tri-M2MT: Multi-modalities based effective acute bilirubin encephalopathy diagnosis through multi-transformer using neonatal Magnetic Resonance Imaging.CAAI Trans. Intell. Technol.202510cit2.1240910.1049/cit2.12409
     [Google Scholar]
  8. AlhussenA. Anul HaqM. Ahmad KhanA. MahendranR.K. KadryS. XAI-RACapsNet: Relevance aware capsule network-based breast cancer detection using mammography images via explainability O-net ROI segmentation.Expert Syst. Appl.202526112546110.1016/j.eswa.2024.125461
     [Google Scholar]
  9. AgrawalT. ChoudharyP. ShankarA. SinghP. DiwakarM. MultiFeNet : Multi-scale feature scaling in deep neural network for the brain tumour classification in MRI images.Int. J. Imaging Syst. Technol.2024341e2295610.1002/ima.22956
     [Google Scholar]
  10. DiwakarM. SinghP. GargD. Edge-guided filtering based CT image denoising using fractional order total variation.Biomed. Signal Process. Control20249210607210.1016/j.bspc.2024.106072
     [Google Scholar]
  11. DiwakarM. KumarP. SinghP. TripathiA. SinghL. An efficient reversible data hiding using SVD over a novel weighted iterative anisotropic total variation based denoised medical images.Biomed. Signal Process. Control20238210456310.1016/j.bspc.2022.104563
     [Google Scholar]
  12. FeurerM. KleinA. EggenspergerK. SpringenbergJ.T. BlumM. HutterF. Auto-sklearn: Efficient and robust automated machine learning.Automated Machine Learning: Methods, Systems, Challenges HutterFrank KotthoffLars VanschorenJoaquin 2019SpringerCham, Switzerland11313410.1007/978‑3‑030‑05318‑5_6
     [Google Scholar]
  13. GangadharanS.M.P. DharaniM. ThapliyalN. YamsaniN. SinghJ. SinghP. Comparative analysis of Deep Learning-based brain tumor prediction models using MRI scan. 2023 Conference on Innovative Sustainable Computational Technologies (3rd International CISCT)Dehradun, India, 08-09 September 2023, pp. 1-610.1109/CISCT57197.2023.10351227
     [Google Scholar]
  14. KhanA.A. MadendranR.K. ThirunavukkarasuU. FaheemM. D 2 PAM : Epileptic seizures prediction using adversarial deep dual patch attention mechanism.CAAI Trans. Intell. Technol.20238375576910.1049/cit2.12261
     [Google Scholar]
  15. KaurB. RakhraM. SharmaN. PrasharD. MrsicL. KhanA.A. KadryS. N-Beats architecture for explainable forecasting of multi-dimensional poultry data.PLoS One2025204e032097910.1371/journal.pone.032097940273069
     [Google Scholar]
  16. BettinelliA. MarturanoF. AvanzoM. LoiE. MenghiE. MezzengaE. PirroneG. SarnelliA. StrigariL. StrolinS. PaiuscoM. A novel benchmarking approach to assess the agreement among radiomic tools.Radiology2022303353354110.1148/radiol.21160435230182
     [Google Scholar]
  17. MahmoodT. SabaT. RehmanA. AlamriF.S. Harnessing the power of radiomics and deep learning for improved breast cancer diagnosis with multiparametric breast mammography.Expert Syst. Appl.202424912374710.1016/j.eswa.2024.123747
     [Google Scholar]
  18. AlshamraniK. AlshamraniH.A. AlqahtaniF.F. AlmutairiB.S. Enhancement of mammographic images using histogram-based techniques for their classification using CNN.Sensors (Basel)202223123510.3390/s2301023536616832
     [Google Scholar]
  19. KimD. JensenL.J. ElgetiT. SteffenI.G. HammB. NagelS.N. Radiomics for everyone: A new tool simplifies creating parametric maps for the visualization and quantification of radiomics features.Tomography20217347748710.3390/tomography703004134564303
     [Google Scholar]
  20. SrivastavaD. RajithaB. AgarwalS. SinghS. Pattern-based image retrieval using GLCM.Neural Comput. Appl.20203215108191083210.1007/s00521‑018‑3611‑1
     [Google Scholar]
  21. LimkinE.J. ReuzéS. CarréA. SunR. SchernbergA. AlexisA. DeutschE. FertéC. RobertC. The complexity of tumor shape, spiculatedness, correlates with tumor radiomic shape features.Sci. Rep.201991432910.1038/s41598‑019‑40437‑530867443
     [Google Scholar]
  22. RamliZ. KarimM.K.A. EffendyN. Abd RahmanM.A. KechikM.M.A. IbahimM.J. HaniffN.S.M. Stability and reproducibility of radiomic features based on various segmentation techniques on cervical cancer DWI-MRI.Diagnostics 20221212312510.3390/diagnostics1212312536553132
     [Google Scholar]
  23. XuL. WangM.Y. QiL. ZouY.F. Fei-YunW.U. SunX.L. Radiomics approach to distinguish between benign and malignant soft tissue tumors on magnetic resonance imaging.Eur. J. Radiol. Open20241210055510.1016/j.ejro.2024.10055538544918
     [Google Scholar]
  24. NahmF.S. Receiver operating characteristic curve: Overview and practical use for clinicians.Korean J. Anesthesiol.2022751253610.4097/kja.2120935124947
     [Google Scholar]
  25. ContiA. DuggentoA. IndovinaI. GuerrisiM. ToschiN. Radiomics in breast cancer classification and prediction.Semin. Cancer Biol.20217223825010.1016/j.semcancer.2020.04.00232371013
     [Google Scholar]
  26. LiH. ZhuM. JianL. BiF. ZhangX. FangC. WangY. WangJ. WuN. YuX. Radiomic score as a potential imaging biomarker for predicting survival in patients with cervical cancer.Front. Oncol.20211170604310.3389/fonc.2021.70604334485139
     [Google Scholar]
  27. ChitaliaR.D. KontosD. Role of texture analysis in breast MRI as a cancer biomarker: A review.J. Magn. Reson. Imaging201949492793810.1002/jmri.2655630390383
     [Google Scholar]
  28. FuscoR. Di BernardoE. PiccirilloA. RubulottaM.R. PetrosinoT. BarrettaM.L. Mattace RasoM. ValloneP. RaianoC. Di GiacomoR. SianiC. AvinoF. ScognamiglioG. Di BonitoM. GranataV. PetrilloA. Radiomic and artificial intelligence analysis with textural metrics extracted by contrast-enhanced mammography and dynamic contrast magnetic resonance imaging to detect breast malignant lesions.Curr. Oncol.20222931947196610.3390/curroncol2903015935323359
     [Google Scholar]
  29. WangK. AnY. ZhouJ. LongY. ChenX. A novel Multi-Level feature selection method for radiomics.Alex. Eng. J.20236699399910.1016/j.aej.2022.10.069
     [Google Scholar]
  30. MaoN. YinP. ZhangH. ZhangK. SongX. XingD. ChuT. Mammography-based radiomics for predicting the risk of breast cancer recurrence: A multicenter study.Br. J. Radiol.20219411272021034810.1259/bjr.2021034834520235
     [Google Scholar]
  31. CuiP. AtheyS. Stable learning establishes some common ground between causal inference and machine learning.Nat. Mach. Intell.20224211011510.1038/s42256‑022‑00445‑z
     [Google Scholar]
  32. UçarM.K. NourM. SindiH. PolatK. The effect of training and testing process on machine learning in biomedical datasets.Math. Probl. Eng.2020202011710.1155/2020/2836236
     [Google Scholar]
  33. PoloT.C.F. MiotH.A. Use of ROC curves in clinical and experimental studies.J. Vasc. Bras.202019e2020018610.1590/1677‑5449.20018634211533
     [Google Scholar]
  34. KhanA.A. MahendranR.K. PerumalK. FaheemM. Dual-3DM3AD: Mixed transformer based semantic segmentation and triplet pre-processing for early multi-class Alzheimer’s diagnosis.IEEE Trans. Neural Syst. Rehabil. Eng.20243269670710.1109/TNSRE.2024.335772338261494
     [Google Scholar]
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Application of Tuning-ensemble N-Best in Auto-Sklearn for Mammographic Radiomic Analysis for Breast Cancer Prediction
Curr. Med. Imaging 21, E15734056400080 (2025); https://doi.org/10.2174/0115734056400080250722024127
/content/journals/cmir/10.2174/0115734056400080250722024127
/content/journals/cmir/10.2174/0115734056400080250722024127
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  • Article Type:     Research Article
Keyword(s): ACM; Auto-Sklearn; Breast cancer; Machine learning; Mammography; Radiomic analysis

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