Refining Breast Cancer Classification: Customized Attention Integration Approaches with Dense and Residual Networks for Enhanced Detection

Overview

Journal Digit Health

Date 2025 Jan 8

PMID 39777055

Authors

Mohammad Sakif Alam

Anwar Hossain Efat

Sm Mahedy Hasan

Md Palash Uddin

Affiliations

Soon will be listed here.

Abstract

Objective: Breast cancer detection is critical for timely and effective treatment, and automatic detection systems can significantly reduce human error and improve diagnosis speed. This study aims to develop an accurate and robust framework for classifying breast cancer into benign and malignant categories using a novel machine learning architecture.

Methods: We propose a dense-ResNet attention integration (DRAI) architecture that combines DenseNet and ResNet models with three attention mechanisms to enhance feature extraction from the BreakHis dataset. The attention mechanisms focus on regionally important features, improving classification accuracy. A triple-level ensemble (TLE) method combines the performance of multiple models, further enhancing prediction accuracy.

Results: The proposed DRAI architecture with TLE achieves an accuracy of 99.58% in classifying breast cancer into benign and malignant categories, surpassing existing methodologies. This high accuracy demonstrates the effectiveness of the fusion architecture and its ability to reduce manual errors in breast cancer diagnosis.

Conclusion: The DRAI architecture with TLE provides a robust, automated framework for breast cancer classification. Its exceptional accuracy lays a solid foundation for future advancements in automated diagnostics and offers a reliable method for aiding early breast cancer detection.

References

Hameed Z, Zahia S, Garcia-Zapirain B, Aguirre J, Maria Vanegas A . Breast Cancer Histopathology Image Classification Using an Ensemble of Deep Learning Models. Sensors (Basel). 2020; 20(16). PMC: 7472736. DOI: 10.3390/s20164373. View

Razmjooy N, Rashid Sheykhahmad F, Ghadimi N . A Hybrid Neural Network - World Cup Optimization Algorithm for Melanoma Detection. Open Med (Wars). 2018; 13:9-16. PMC: 5850997. DOI: 10.1515/med-2018-0002. View

Siegel R, Miller K, Jemal A . Cancer statistics, 2018. CA Cancer J Clin. 2018; 68(1):7-30. DOI: 10.3322/caac.21442. View

Masud M, Rashed A, Hossain M . Convolutional neural network-based models for diagnosis of breast cancer. Neural Comput Appl. 2020; 34(14):11383-11394. PMC: 7545025. DOI: 10.1007/s00521-020-05394-5. View

Wang Y, Choi E, Choi Y, Zhang H, Jin G, Ko S . Breast Cancer Classification in Automated Breast Ultrasound Using Multiview Convolutional Neural Network with Transfer Learning. Ultrasound Med Biol. 2020; 46(5):1119-1132. DOI: 10.1016/j.ultrasmedbio.2020.01.001. View

Huynh P, Jarolimek A, Daye S . The false-negative mammogram. Radiographics. 1998; 18(5):1137-54; quiz 1243-4. DOI: 10.1148/radiographics.18.5.9747612. View

Yan R, Ren F, Wang Z, Wang L, Zhang T, Liu Y . Breast cancer histopathological image classification using a hybrid deep neural network. Methods. 2019; 173:52-60. DOI: 10.1016/j.ymeth.2019.06.014. View

Spanhol F, Oliveira L, Petitjean C, Heutte L . A Dataset for Breast Cancer Histopathological Image Classification. IEEE Trans Biomed Eng. 2015; 63(7):1455-62. DOI: 10.1109/TBME.2015.2496264. View

Gao W, Wang D, Huang Y . Designing a Deep Learning-Driven Resource-Efficient Diagnostic System for Metastatic Breast Cancer: Reducing Long Delays of Clinical Diagnosis and Improving Patient Survival in Developing Countries. Cancer Inform. 2023; 22:11769351231214446. PMC: 10683375. DOI: 10.1177/11769351231214446. View

10.

Jiang Y, Chen L, Zhang H, Xiao X . Breast cancer histopathological image classification using convolutional neural networks with small SE-ResNet module. PLoS One. 2019; 14(3):e0214587. PMC: 6440620. DOI: 10.1371/journal.pone.0214587. View

11.

Davoudi K, Thulasiraman P . Evolving convolutional neural network parameters through the genetic algorithm for the breast cancer classification problem. Simulation. 2021; 97(8):511-527. PMC: 8293734. DOI: 10.1177/0037549721996031. View

12.

Xu Z, Rashid Sheykhahmad F, Ghadimi N, Razmjooy N . Computer-aided diagnosis of skin cancer based on soft computing techniques. Open Med (Wars). 2020; 15(1):860-871. PMC: 7711880. DOI: 10.1515/med-2020-0131. View

13.

Deniz E, Sengur A, Kadiroglu Z, Guo Y, Bajaj V, Budak U . Transfer learning based histopathologic image classification for breast cancer detection. Health Inf Sci Syst. 2018; 6(1):18. PMC: 6162199. DOI: 10.1007/s13755-018-0057-x. View

14.

Veta M, Pluim J, van Diest P, Viergever M . Breast cancer histopathology image analysis: a review. IEEE Trans Biomed Eng. 2014; 61(5):1400-11. DOI: 10.1109/TBME.2014.2303852. View

15.

LeCun Y, Bengio Y, Hinton G . Deep learning. Nature. 2015; 521(7553):436-44. DOI: 10.1038/nature14539. View

16.

Obayya M, Maashi M, Nemri N, Mohsen H, Motwakel A, Osman A . Hyperparameter Optimizer with Deep Learning-Based Decision-Support Systems for Histopathological Breast Cancer Diagnosis. Cancers (Basel). 2023; 15(3). PMC: 9913140. DOI: 10.3390/cancers15030885. View

17.

Arun Kumar S, Sasikala S . Review on Deep Learning-Based CAD Systems for Breast Cancer Diagnosis. Technol Cancer Res Treat. 2023; 22:15330338231177977. PMC: 10272643. DOI: 10.1177/15330338231177977. View

18.

Hochreiter S, Schmidhuber J . Long short-term memory. Neural Comput. 1997; 9(8):1735-80. DOI: 10.1162/neco.1997.9.8.1735. View

19.

Byra M, Galperin M, Ojeda-Fournier H, Olson L, OBoyle M, Comstock C . Breast mass classification in sonography with transfer learning using a deep convolutional neural network and color conversion. Med Phys. 2018; 46(2):746-755. PMC: 8544811. DOI: 10.1002/mp.13361. View

20.

Efat A, Hasan S, Uddin M, Mamun M . A Multi-level ensemble approach for skin lesion classification using Customized Transfer Learning with Triple Attention. PLoS One. 2024; 19(10):e0309430. PMC: 11500880. DOI: 10.1371/journal.pone.0309430. View