Enhancing breast cancer detection: a novel deep learning approach using hybrid convolutional neural networks and residual number systems

Document Type : Original Article

Authors

1 1.Department of Computer Engineering, Kerman Branch, Islamic Azad University, Kerman, Iran. B.rezai@kmu.ac.ir

2 Department of Computer Engineering, Kerman Branch, Islamic Azad University, Kerman, Iran

3 Department of Computer Engineering, Graduate University of Advanced Technology, Kerman, Iran

10.30476/jhmi.2024.103601.1232

Abstract

Background: The detection of breast cancer is vital for intervention and treatment as soon as possible. This study attempts to use a new hybrid deep learning approach that is a combination of Convolutional Neural Networks (CNNs) and Residual Number Systems (RNS) to more precisely detect cancer of the breasts.
Methods: INBREAST and MINI-DDSM datasets were employed to evaluate the hybrid model. Precision, recall, F1-score, and accuracy of these were employed to determine effects of the model compared to existing methods.
Results: The hybrid model was found to be 99% accurate in training using INBREAST dataset, and 91.5% in validation using INBREAST dataset, while MINI-DDSM dataset was found to be 98% in training and 95.02% in validation in terms of accuracy. The model was superior in MINI-DDSM dataset compared to existing models such as ZFNET and ResNet18 in precision, recall, and accuracy metrics. INBREAST dataset was hard to manage due to its nature of complexity, hence it was found to produce low precision and recall despite having high overall precision in performance.
Conclusion: This study highlights the potential of the proposed hybrid deep learning approach for breast cancer detection, especially in simpler datasets. Future research should focus on techniques such as data augmentation, transfer learning, and ensemble methods to improve robustness and generalizability across diverse imaging scenarios. The findings contribute to the integration of deep learning in medical diagnostics, aiming for more accurate and efficient breast cancer detection systems.

Keywords

Main Subjects

References

  1. Mo Y, Han C, Liu Y, Liu M, Shi Z, Lin J, et al. HoVer-Trans: Anatomy-Aware HoVer-Transformer for ROI-Free Breast Cancer Diagnosis in Ultrasound Images. IEEE Trans Med Imaging. 2023;42(6):1696-706. doi: 10.1109/TMI.2023.3236011.

  2. Masud M, Hossain MS, Alhumyani H, Alshamrani SS, Cheikhroubou O, Ibrahim S, et al. Pre-trained convolutional neural networks for breast cancer detection using ultrasound images. ACM Transactions on Internet Technology (TOIT). 2021;21(4):1-17. doi: 10.1145/3418355.

  3. Kirubakaran R, Jia TC, Aris NM. Awareness of breast cancer among surgical patients in a tertiary hospital in Malaysia. Asian Pacific journal of cancer prevention: APJCP. 2017;18(1):115.

  4. Stalin MS, Kalaimagal R. Breast Cancer Diagnosis from Low Intensity Asymmetry Thermogram Breast Images using Fast Support Vector Machine. i-manager’s Journal on Image Processing. 2016;3(3):17. doi: 10.26634/jip.3.3.8151.

  5. Rajesh S, Choudhury NA, Moulik S, editors. Hepatocellular Carcinoma (HCC) Liver Cancer prediction using Machine Learning Algorithms. 2020 IEEE 17th India Council International Conference (INDICON); 2020 10-13 Dec. 2020.

  6. Islam MZ, Islam MM, Asraf A. A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images. Inform Med Unlocked. 2020;20:100412. doi: 10.1016/j.imu.2020.100412.

  7. Kwok S, editor Multiclass classification of breast cancer in whole-slide images. Image Analysis and Recognition: 15th International Conference, ICIAR 2018, Póvoa de Varzim, Portugal, June 27–29, 2018, Proceedings 15; 2018.

  8. Awotunde JB, Ajagbe SA, Oladipupo MA, Awokola JA, Afolabi OS, Mathew TO, et al., editors. An improved machine learnings diagnosis technique for COVID-19 pandemic using chest X-ray images. International Conference on Applied Informatics; 2021. doi: 10.1007/978-3-030-89654-6_23.

  9. Clancy E. ACS Report Shows Prostate Cancer on the Rise, Cervical Cancer on the Decline. Rental & Urology News. 2023.

  10. Cardarilli GC, Nannarelli A, Re M, editors. Residue number system for low-power DSP applications. 2007 Conference Record of the Forty-First Asilomar Conference on Signals, Systems and Computers; 2007. doi: 10.1109/ACSSC.2007.4487461.

  11. Samimi N, Kamal M, Afzali-Kusha A, Pedram M. Res-DNN: A residue number system-based DNN accelerator unit. IEEE Transactions on Circuits and Systems I: regular papers. 2019;67(2):658-71. doi: 10.1109/TCSI.2019.2951083.

  12. Salamat S, Imani M, Gupta S, Rosing T, editors. Rnsnet: In-memory neural network acceleration using residue number system. 2018 IEEE International Conference on Rebooting Computing (ICRC); 2018. doi: 10.1109/ICRC.2018.8638592.

  13. Nakahara H, Sasao T, editors. A deep convolutional neural network based on nested residue number system. 2015 25th International Conference on Field Programmable Logic and Applications (FPL); 2015. doi: 10.1109/FPL.2015.7293933.

  14. Anitha K, Arulananth T, Karthik R, Reddy PB. Design and implementation of modified sequential parallel rns forward converters. International Journal of Applied Engineering Research. 2017;12(16):6159-63.

  15. de Matos R, Paludo R, Chervyakov N, Lyakhov PA, Pettenghi H, editors. Efficient implementation of modular multiplication by constants applied to RNS reverse converters. 2017 IEEE International Symposium on Circuits and Systems (ISCAS); 2017. doi: 10.1109/ISCAS.2017.8050779.

  16. Vang YS, Chen Z, Xie X, editors. Deep learning framework for multi-class breast cancer histology image classification. Image Analysis and Recognition: 15th International Conference, ICIAR 2018, Póvoa de Varzim, Portugal, June 27–29, 2018, Proceedings 15; 2018.

  17. Rakhlin A, Shvets A, Iglovikov V, Kalinin AA, editors. Deep convolutional neural networks for breast cancer histology image analysis. Image Analysis and Recognition: 15th International Conference, ICIAR 2018, Póvoa de Varzim, Portugal, June 27–29, 2018, Proceedings 15; 2018. doi: 10.1101/259911.

  18. Qian L, Bai J, Huang Y, Zeebaree DQ, Saffari A, Zebari DA. Breast cancer diagnosis using evolving deep convolutional neural network based on hybrid extreme learning machine technique and improved chimp optimization algorithm. Biomedical Signal Processing and Control. 2024;87:105492. doi: 10.1016/j.bspc.2023.105492.

  19. Venkatesan R, Shao YS, Wang M, Clemons J, Dai S, Fojtik M, et al., editors. Magnet: A modular accelerator generator for neural networks. 2019 IEEE/ACM International Conference on Computer-Aided Design (ICCAD); 2019. doi: 10.1109/ICCAD45719.2019.8942127.

  20. Capra M, Bussolino B, Marchisio A, Shafique M, Masera G, Martina M. An updated survey of efficient hardware architectures for accelerating deep convolutional neural networks. Future Internet. 2020;12(7):113. doi: 10.3390/fi12070113.

  21. Genc H, Haj-Ali A, Iyer V, Amid A, Mao H, Wright J, et al. Gemmini: An agile systolic array generator enabling systematic evaluations of deep-learning architectures. arXiv preprint arXiv:191109925. 2019;3(25):15-7.

  22. Wi H, Kim H, Choi S, Kim L-S, editors. Compressing sparse ternary weight convolutional neural networks for efficient hardware acceleration. 2019 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED); 2019. doi: 10.1109/ISLPED.2019.8824855.

  23. Wang K, Liu Z, Lin Y, Lin J, Han S, editors. Haq: Hardware-aware automated quantization with mixed precision. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition; 2019. doi: 10.1109/CVPR.2019.00881.

  24. Pagliari DJ, Macii E, Poncino M, editors. Dynamic bit-width reconfiguration for energy-efficient deep learning hardware. Proceedings of the International Symposium on Low Power Electronics and Design; 2018. doi: 10.1145/3218603.3218611.

  25. Nazemi M, Pedram M, editors. Deploying customized data representation and approximate computing in machine learning applications. Proceedings of the International Symposium on Low Power Electronics and Design; 2018. doi: 10.1145/3218603.3218612.

  26. Park E, Kim D, Kim S, Kim Y-D, Kim G, Yoon S, et al., editors. Big/little deep neural network for ultra low power inference. 2015 international conference on hardware/software codesign and system synthesis (codes+ isss); 2015. doi: 10.1109/CODESISSS.2015.7331375.

  27. Umuroglu Y, Fraser NJ, Gambardella G, Blott M, Leong P, Jahre M, et al., editors. Finn: A framework for fast, scalable binarized neural network inference. Proceedings of the 2017 ACM/SIGDA international symposium on field-programmable gate arrays; 2017. doi: 10.1145/3020078.3021744.

  28. Ding R, Liu Z, Shi R, Marculescu D, Blanton R, editors. Lightnn: Filling the gap between conventional deep neural networks and binarized networks. Proceedings of the Great Lakes Symposium on VLSI 2017; 2017. doi: 10.1145/3060403.3060465.

  29. Khoshavi N, Broyles C, Bi Y, editors. Compression or corruption? a study on the effects of transient faults on bnn inference accelerators. 2020 21st International Symposium on Quality Electronic Design (ISQED); 2020. doi: 10.1109/ISQED48828.2020.9137006.

  30. Moreira IC, Amaral I, Domingues I, Cardoso A, Cardoso MJ, Cardoso JS. INbreast: toward a full-field digital mammographic database. Acad Radiol. 2012;19(2):236-48. doi: 10.1016/j.acra.2011.09.014.

  31. Arora R, Rai PK, Raman B. Deep feature-based automatic classification of mammograms. Med Biol Eng Comput. 2020;58(6):1199-211. doi: 10.1007/s11517-020-02150-8.

  32. Dhungel N, Carneiro G, Bradley AP, editors. The automated learning of deep features for breast mass classification from mammograms. Medical Image Computing and Computer-Assisted Intervention-MICCAI 2016: 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part II 19; 2016.

  33. Dhungel N, Carneiro G, Bradley AP. A deep learning approach for the analysis of masses in mammograms with minimal user intervention. Med Image Anal. 2017;37:114-28. doi: 10.1016/j.media.2017.01.009.

  34. Lee CY, Chen GL, Zhang ZX, Chou YH, Hsu CC. Is Intensity Inhomogeneity Correction Useful for Classification of Breast Cancer in Sonograms Using Deep Neural Network? J Healthc Eng. 2018;2018:8413403. doi: 10.1155/2018/8413403.

  35. Al-Dhabyani W, Gomaa M, Khaled H, Aly F. Deep learning approaches for data augmentation and classification of breast masses using ultrasound images. Int J Adv Comput Sci Appl. 2019;10(5):1-11. doi: 10.14569/IJACSA.2019.0100579.

  36. Stalin MS, Kalaimagal R. Breast Cancer Diagnosis from Low Intensity Asymmetry Thermogram Breast Images using Fast Support Vector Machine. i-manager’s Journal on Image Processing. 2016;3(3):17.

  37. Caplan L. Delay in breast cancer: implications for stage at diagnosis and survival. Front Public Health. 2014;2:87. doi: 10.3389/fpubh.2014.00087.

  38. Xie X, Shi F, Niu J, Tang X, editors. Breast ultrasound image classification and segmentation using convolutional neural networks. Advances in Multimedia Information Processing-PCM 2018: 19th Pacific-Rim Conference on Multimedia, Hefei, China, September 21-22, 2018, Proceedings, Part III 19; 2018.

  39. Sharmin S, Ahammad T, Talukder MA, Ghose P. A hybrid dependable deep feature extraction and ensemble-based machine learning approach for breast cancer detection. IEEE Access. 2023;11:87694-708. doi: 10.1109/ACCESS.2023.3304628.

  40. Sahu A, Das PK, Meher S. High accuracy hybrid CNN classifiers for breast cancer detection using mammogram and ultrasound datasets. Biomedical Signal Processing and Control. 2023;80:104292. doi: 10.1016/j.bspc.2022.104292.

  41. Altaf MM. A hybrid deep learning model for breast cancer diagnosis based on transfer learning and pulse-coupled neural networks. Math Biosci Eng. 2021;18(5):5029-46. doi: 10.3934/mbe.2021256.

  42. Raaj RS. Breast cancer detection and diagnosis using hybrid deep learning architecture. Biomedical Signal Processing and Control. 2023;82:104558. doi: 10.1016/j.bspc.2022.104558.

  43. Swetha A, Bala M, Sharma K, Katarya R, editors. An enhanced hybrid model paradigm for transforming breast cancer prediction. 2023 17th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS); 2023. doi: 10.1109/SITIS61268.2023.00054.

  44. Aslan MF. A hybrid end-to-end learning approach for breast cancer diagnosis: convolutional recurrent network. Computers and Electrical Engineering. 2023;105:108562. doi: 10.1016/j.compeleceng.2022.108562.

  45. Alzubaidi L, Al-Shamma O, Fadhel MA, Farhan L, Zhang J, Duan Y. Optimizing the performance of breast cancer classification by employing the same domain transfer learning from hybrid deep convolutional neural network model. Electronics. 2020;9(3):445. doi: 10.3390/electronics9030445.

  46. Romero M, Interian Y, Solberg T, Valdes G. Training deep learning models with small datasets. arXiv preprint arXiv:191206761. 2019.

  47. Rorabaugh AK, Caino-Lores S, Johnston T, Taufer M. High frequency accuracy and loss data of random neural networks trained on image datasets. Data Brief. 2022;40:107780. doi: 10.1016/j.dib.2021.107780.

  48. Adedigba AP, Adeshina SA, Abhinu AM. Performance Evaluation of Deep Learning Models on Mammogram Classification Using Small Dataset. Bioengineering (Basel). 2022;9(4). doi: 10.3390/bioengineering9040161.

  49. Yu X, Tian J, Chen Z, Meng Y, Zhang J. Predictive breast cancer diagnosis using ensemble fuzzy model. Image and Vision Computing. 2024;148:105146. doi: 10.1016/j.imavis.2024.105146.

  50. Aboutalib SS, Mohamed AA, Berg WA, Zuley ML, Sunkin JH, Wu S. Deep Learning to Distinguish Recalled but Benign Mammography Images in Breast Cancer Screening. Clin Cancer Res. 2018;24(23):5902-9. doi: 10.1158/1078-0432.CCR-18-1115.

  51. Shams S, Platania R, Zhang J, Kim J, Lee K, Park S-J, editors. Deep generative breast cancer screening and diagnosis. Medical Image Computing and Computer Assisted Intervention-MICCAI 2018: 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part II 11; 2018.

  52. Chen Q, Liu J, Luo K, Zhang X, Wang X, editors. Transfer deep learning mammography diagnostic model from public datasets to clinical practice: a comparison of model performance and mammography datasets. 14th International workshop on breast imaging (IWBI 2018); 2018.

  53. Razali NF, Isa IS, Sulaiman SN, Karim NKA, Osman MK, editors. High-level features in deeper deep learning layers for breast cancer classification. 2021 11th IEEE International Conference on Control System, Computing and Engineering (ICCSCE); 2021. doi: 10.1109/ICCSCE52189.2021.9530911.

Health Management & Information Science
Volume 11, Issue 3
July 2024
Pages 115-129

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