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

Introduction:

Recently, deep learning (DL) algorithms use Arithmetic Units (AU) in CPU/GPU hardware for processing images/data. AU operates in fixed precision and limits the representation of weights and activations in DL. The problem leads to quantization errors, which reduce accuracy during cancer cell segmentation.

Methods:

In this study, arithmetic multiplication in convolution layers is replaced with Vedic multiplication in the proposed DnCNN algorithm. Next, Vedic multiplication-based convolution layers in the DnCNN architecture are optimized using POA (Pelican Optimization Algorithm), and the resulting POA-DnCNN is implemented on an FPGA device for breast cancer detection, segmentation, and classification of benign and malignant breast lesions.

Discussion:

In the convolution layer of DnCNN, floating-point operations are performed through the Hybrid-Vedic (HV) multiplier called ‘CUTIN,’ which is the combination of and with the upasutra ‘.’ Larger image sizes increase processor size and gate count.

Results:

The proposed HV-FPGA-based breast cancer detection system, employing Vedic multiplication in the convolution layers of DnCNN and hyperparameters optimized by POA, detects stages of breast cancer with an accuracy of 96.3%, precision of 94.54%, specificity of 92.37%, F-score of 93.56%, IoU of 94.78%, and DSC of 95.45%, outperforming existing methods.

Conclusion:

The proposed CUTIN multiplier uses a CSA (carry save adder) with simplified sum-carry generation logic (CSCGL), achieving lower area-delay, high speed, and improved precision.

© The Author(s). Published by Bentham Science Publisher.
This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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2025-09-08

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References

  1. YamunaV. MonikaM. AmrishaR.R. KumarA. SasikalaS. Towards improving breast cancer detection in ultrasound images using deep transfer learning.2023 2nd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA)Coimbatore, India, 2023, pp. 1-6.10.1109/ICAECA56562.2023.10200302
     [Google Scholar]
  2. IslamM.S. YasminS. ChowdhuryT.A. SayemA. MimK. AI-assisted breast cancer diagnosis: Deep learning on RSNA mammogram images for improved classification.2023 26th International Conference on Computer and Information Technology (ICCIT)Cox's Bazar, Bangladesh, 2023, pp. 1-5.10.1109/ICCIT60459.2023.10441145
     [Google Scholar]
  3. MomeniA. HanJ. MontuschiP. LombardiF. Design and analysis of approximate compressors for multiplication.IEEE Trans. Comput.201564498499410.1109/TC.2014.2308214
     [Google Scholar]
  4. KalyanapuS. KandulaA.R. MadhuriP. GantaP. VattikutiH.N.S.S. DarapuU. Enhancing breast cancer prediction through SVM-based analysis.2023 Annual International Conference on Emerging Research Areas: International Conference on Intelligent Systems (AICERA/ICIS)Kanjirapally, India, 2023, pp. 1-6.10.1109/AICERA/ICIS59538.2023.10420106
     [Google Scholar]
  5. VenkataramaniS ChakradharST RoyK RaghunathanA Computing approximately and efficiently.2015 Design, Automation & Test in Europe Conference & Exhibition (DATE)Grenoble, France, 09-13 March 2015, pp. 748-751.10.7873/DATE.2015.1113
     [Google Scholar]
  6. ShahS.M. KhanR.A. ArifS. SajidU. Artificial intelligence for breast cancer analysis: Trends & directions.Comput. Biol. Med.202214210522110.1016/j.compbiomed.2022.10522135016100
     [Google Scholar]
  7. MahmoodT. LiJ. PeiY. AkhtarF. An automated in-depth feature learning algorithm for breast abnormality prognosis and robust characterization from mammography images using deep transfer learning.Biology202110985910.3390/biology1009085934571736
     [Google Scholar]
  8. LiuC. HuS.C. WangC. LafataK. YinF.F. Automatic detection of pulmonary nodules on CT images with YOLOv3: Development and evaluation using simulated and patient data.Quant. Imaging Med. Surg.202010101917192910.21037/qims‑19‑88333014725
     [Google Scholar]
  9. GuoY.Y. HuangY.H. WangY. HuangJ. LaiQ.Q. LiY.Z. Breast MRI tumor automatic segmentation and triple‐negative breast cancer discrimination algorithm based on deep learning.Comput. Math. Methods Med.2022202211910.1155/2022/254135836092784
     [Google Scholar]
  10. JiangZ. GandomkarZ. TrieuP.D.Y. Tavakoli TabaS. BarronM.L. ObeidyP. LewisS.J. Evaluating and recalibrating AI models for breast cancer diagnosis in a new context: Insights from transfer learning, image enhancement, and high-quality training data integration.Cancers202416232210.3390/cancers1602032238254813
     [Google Scholar]
  11. AzourF. BoukercheA. Design guidelines for mammogram-based computer-aided systems using deep learning techniques.IEEE Access202210217012172610.1109/ACCESS.2022.3151830
     [Google Scholar]
  12. AvcıH. KarakayaJ. A novel medical image enhancement algorithm for breast cancer detection on mammography images using machine learning.Diagnostics202313334810.3390/diagnostics1303034836766453
     [Google Scholar]
  13. IranmakaniS. MortezazadehT. SajadianF. GhazianiM.F. GhafariA. KhezerlooD. MusaA.E. A review of various modalities in breast imaging: Technical aspects and clinical outcomes.Egypt. J. Radiol. Nucl. Med.20205115710.1186/s43055‑020‑00175‑5
     [Google Scholar]
  14. HeidariM. KhuzaniA.Z. HollingsworthA.B. DanalaG. MirniaharikandeheiS. QiuY. LiuH. ZhengB. Prediction of breast cancer risk using a machine learning approach embedded with a locality preserving projection algorithm.Phys. Med. Biol.201863303502010.1088/1361‑6560/aaa1ca29239858
     [Google Scholar]
  15. KhuriwalN. MishraN. Breast cancer detection from histopathological images using deep learning.2018 3rd International Conference and Workshops on Recent Advances and Innovations in Engineering (ICRAIE).Jaipur, India, 22-25 November 2018, pp. 1-4.10.1109/ICRAIE.2018.8710426
     [Google Scholar]
  16. HousseinE.H. EmamM.M. AliA.A. An efficient multilevel thresholding segmentation method for thermography breast cancer imaging based on improved chimp optimization algorithm.Expert Syst. Appl.202118511565110.1016/j.eswa.2021.115651
     [Google Scholar]
  17. GoudarJ. BirasalA.H. Design of floating-point multiplier using modified Wallace & Dadda algorithms.Int J Latest Technol Eng Manag Appl Sci2018VIIIII211223
     [Google Scholar]
  18. PravallikaM. KrishnaV.V.M. Design and verification of high-speed and efficient asynchronous floating-point multiplier.IJ of Engineering Research and Technology.2013
     [Google Scholar]
  19. PangW.L. ChanK.Y. WongS.K. TanC.S. VHDL modeling of Booth radix-4 floating-point multiplier for VLSI designer’s library.WSEAS Trans. on Systems201312678688
     [Google Scholar]
  20. SimićS. BemporadA. InversoO. TribastoneM. Tight error analysis in fixed-point arithmetic.Form. Asp. Comput.202234113210.1145/3524051
     [Google Scholar]
  21. JinQ. RenJ. ZhuangR. HanumanteS. LiZ. ChenZ. WangY. YangK. TulyakovS. F8net: Fixed-point 8-bit only multiplication for network quantization.arXiv preprint20222202.05239
     [Google Scholar]
  22. TongJ.Y. NagleD. RutenbarR.A. Reducing power by optimizing the necessary precision/range of floating-point arithmetic.IEEE Trans Very Large Scale Integr (VLSI) Syst2000Jun8327328610.1109/92.845894
     [Google Scholar]
  23. LiH. A single precision floating-point multiplier for machine learning hardware acceleration.2021 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS)Shenyang, China, 10-11 December 2021, pp. 674-677.10.1109/TOCS53301.2021.9688936
     [Google Scholar]
  24. AkkurE. TurkF. ErogulO. Breast cancer diagnosis using feature selection approaches and Bayesian optimization.Comput. Syst. Sci. Eng.20234521017103110.32604/csse.2023.033003
     [Google Scholar]
  25. RajuN.G. RaoP.N. Particle swarm optimization methods for image segmentation applied in mammography.Int. J. Eng. Res. Appl.20133615721579
     [Google Scholar]
  26. AliY. Effective BCDNet-based breast cancer classification model using hybrid deep learning with VGG16-based optimal feature extraction.BMC Med. Imaging202525112310.1186/s12880‑024‑01541‑939748339
     [Google Scholar]
  27. OyeladeO.N. AminuE.F. WangH. RaffertyK. An adaptation of hybrid binary optimization algorithms for medical image feature selection in neural network for classification of breast cancer.Neurocomputing202561712901810.1016/j.neucom.2024.129018
     [Google Scholar]
  28. Breast Ultrasound Images Dataset(BUSI).Available from: https://www.kaggle.com/datasets/sabahesaraki/breast-ultrasound-images-dataset
  29. TianC. XuY. LiZ. ZuoW. FeiL. LiuH. Attention-guided CNN for image denoising.Neural Netw.202012411712910.1016/j.neunet.2019.12.02431991307
     [Google Scholar]
  30. TianC. XuY. ZuoW. Image denoising using deep CNN with batch renormalization.Neural Netw.202012146147310.1016/j.neunet.2019.08.02231629201
     [Google Scholar]
  31. QuanY. ChenY. ShaoY. TengH. XuY. JiH. Image denoising using complex-valued deep CNN.Pattern Recognit.202111110763910.1016/j.patcog.2020.107639
     [Google Scholar]
  32. AhnB. ChoN.I. Block-matching convolutional neural network for image denoising.arXiv preprint20171704.00524
     [Google Scholar]
  33. SilvanoC. IelminiD. FerrandiF. FiorinL. CurzelS. BeniniL. ContiF. GarofaloA. ZambelliC. CaloreE. SchifanoS. A survey on deep learning hardware accelerators for heterogeneous HPC platforms.ACM Comput. Surv.2023
     [Google Scholar]
  34. ParachaS.Q. HaqM.I. FarzandH.T. NaqviS.T.G. NiaziS.A. MunirA. FaridM.H. Hybrid analog-digital neural network processor for efficient AI computing.Sch. J. Eng. Technol.202513317618610.36347/sjet.2025.v13i03.003
     [Google Scholar]
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Optimised Convolution Layers of DnCNN using Vedic Multiplier and Hyperparameter Tuning in Cancer Detection on Field Programmable Gate Array
Curr. Med. Imaging 21, E15734056400656 (2025); https://doi.org/10.2174/0115734056400656250616073019
/content/journals/cmir/10.2174/0115734056400656250616073019
/content/journals/cmir/10.2174/0115734056400656250616073019
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  • Article Type:     Research Article
Keyword(s): Breast cancer segmentation; CNN; FPGA; Nikhilam with upasutra; Urdhva Tryambakam Sutra; Vedic multiplication

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