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Mobile Networks and Applications

05-03-2024 | Research

CSO-CNN: Cat Swarm Optimization-guided Convolutional Neural Network for Mobile Detection of Breast Cancer

Authors: Xiaoyan Jiang, Zuojin Hu, Zhaozhao Xu

Published in: Mobile Networks and Applications

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Abstract

Breast cancer has become the most common cancer in the world. Early diagnosis and treatment can greatly improve the survival rate of breast cancer patients. Computer diagnostic technology based on convolutional neural networks (CNNs) can assist in detecting breast cancer based on medical images, effectively improving detection accuracy. Hyperparameters in CNN will affect model performance, so hyperparameter tuning is necessary for model training. However, traditional tuning methods can get stuck in local minimums. Therefore, the weights and biases of artificial neural networks are usually trained using global optimization algorithms. Our research introduces cat swarm optimization (CSO) to construct a cat swarm optimization-guided convolutional neural network (CSO-CNN). The model can quickly obtain the optimal combination of hyperparameters and stably get closer to the global optimal. The statistical results of CSO-CNN obtained a sensitivity of 93.50% ± 2.42%, a specificity of 92.20% ± 3.29%, a precision of 92.35% ± 3.01%, an accuracy of 92.85% ± 2.49%, an F1-score of 92.91% ± 2.44%, Matthews correlation coefficient of 85.74% ± 4.94%, and Fowlkes-Mallows index was 92.92% ± 2.43%. Our CSO-CNN algorithm is superior to five state-of-the-art methods. In addition, we tested the CSO-CNN algorithm on the local computer to simulate the mobile environment and confirmed that the algorithm can be transplanted to the network servers.

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Viswanathan S, Parida S, Lingipilli BT, Krishnan R, Podipireddy DR et al (2023) Role of Gut Microbiota in Breast Cancer and Drug Resistance. Pathogens 12(3):468. https://doi.org/10.3390/pathogens12030468CrossRefPubMedPubMedCentral

Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I et al (2021) Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer J Clin 71(3):209–249. https://doi.org/10.3322/caac.21660CrossRef

Rahman WT, Gerard S, Grundlehner P, Oudsema R, McLaughlin C et al (2023) Outcomes of high-risk breast MRI screening in women without prior history of breast cancer: Effectiveness data from a tertiary care center. J Breast Imaging 6(1):53–63. https://doi.org/10.1093/jbi/wbad092CrossRef

Feng R-Q, Li D-H, Liu X-K, Zhao X-H, Wen Q-E et al. (2023) Traditional chinese medicine for breast cancer: a review. Breast cancer: targets therapy 15:747–759. https://doi.org/10.2147/BCTT.S429530

Diessner J, Anders L, Herbert S, Kiesel M, Bley T et al (2023) Evaluation of different imaging modalities for axillary lymph node staging in breast cancer patients to provide a personalized and optimized therapy algorithm. J Cancer Res Clin Oncol 149(7):3457–3467. https://doi.org/10.1007/s00432-022-04221-9CrossRefPubMed

Shareef M, Ashraf MA, Sarfraz M (2016) Natural cures for breast cancer treatment. Saudi Pharmaceut J 24(3):233–240. https://doi.org/10.1016/j.jsps.2016.04.018CrossRef

Xu H, Xu B (2023) Breast cancer: Epidemiology, risk factors and screening. Chin J Cancer Res 35(6):565–583. https://doi.org/10.21147/j.issn.1000-9604.2023.06.02CrossRefPubMedPubMedCentral

Debelee TG, Schwenker F, Ibenthal A, Yohannes D (2020) Survey of deep learning in breast cancer image analysis. Evol Syst 11:143–163. https://doi.org/10.1007/s12530-019-09297-2CrossRef

Sun L, Wang J, Hu Z, Xu Y, Cui Z (2019) Multi-view convolutional neural networks for mammographic image classification. IEEE Access 7:126273–126282. https://doi.org/10.1109/ACCESS.2019.2939167CrossRef

10.

Hoff SR, Abrahamsen A-L, Samset JH, Vigeland E, Klepp O et al (2012) Breast cancer: missed interval and screening-detected cancer at full-field digital mammography and screen-film mammography—results from a retrospective review. Radiology 264(2):378–386. https://doi.org/10.1148/radiol.12112074CrossRefPubMed

11.

Friedewald SM, Rafferty EA, Rose SL, Durand MA, Plecha DM et al (2014) Breast cancer screening using tomosynthesis in combination with digital mammography. JAMA 311(24):2499–2507. https://doi.org/10.1001/jama.2014.6095CrossRefPubMed

12.

Gordon PB (2002) Ultrasound for breast cancer screening and staging. Radiologic Clinics 40(3):431–441. https://doi.org/10.1016/S0033-8389(01)00014-8CrossRefPubMed

13.

Houssami N, Hayes DF (2009) Review of preoperative magnetic resonance imaging (MRI) in breast cancer: should MRI be performed on all women with newly diagnosed, early stage breast cancer? CA: A Cancer J Clin 59(5):290–302. https://doi.org/10.3322/caac.20028CrossRef

14.

Skaane P (2017) Breast cancer screening with digital breast tomosynthesis. Breast cancer 24:32–41. https://doi.org/10.1007/s12282-016-0699-y

15.

Tan YJ, Sim KS, Ting FF (2017) Breast cancer detection using convolutional neural networks for mammogram imaging system. In 2017 International conference on robotics, automation and sciences (ICORAS). Melaka, Malaysia IEEE. 1–5. https://doi.org/10.1109/ICORAS.2017.8308076

16.

Raaj RS (2023) Breast cancer detection and diagnosis using hybrid deep learning architecture. Biomed Signal Process Control 82:104558. https://doi.org/10.1016/j.bspc.2022.104558CrossRef

17.

Melekoodappattu JG, Dhas AS, Kandathil BK, Adarsh KS (2023) Breast cancer detection in mammogram: combining modified CNN and texture feature based approach. J Ambient Intell Humaniz Comput 14(9):11397–11406. https://doi.org/10.1007/s12652-022-03713-3CrossRef

18.

Lecun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324. https://doi.org/10.1109/5.726791CrossRef

19.

Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504–507. https://doi.org/10.1126/science.1127647ADSMathSciNetCrossRefPubMed

20.

Gu J, Wang Z, Kuen J, Ma L, Shahroudy A et al (2018) Recent advances in convolutional neural networks. Pattern Recogn 77:354–377ADSCrossRef

21.

Mirjalili S (2019) Genetic algorithm. In evolutionary algorithms and neural networks. Springer, Cham, Switzerland 780:43–55. https://doi.org/10.1007/978-3-319-93025-1_4

22.

Pereira DC, Ramos RP, Do Nascimento MZ (2014) Segmentation and detection of breast cancer in mammograms combining wavelet analysis and genetic algorithm. Comput Methods Programs Biomed 114(1):88–101. https://doi.org/10.1016/j.cmpb.2014.01.014CrossRefPubMed

23.

Eberhart R, Kennedy J (1995) A new optimizer using particle swarm theory. In MHS'95. Proceedings of the sixth international symposium on micro machine and human science. Nagoya, Japan IEEE. 39–43. https://doi.org/10.1109/MHS.1995.494215

24.

Sakri SB, Rashid NBA, Zain ZM (2018) Particle swarm optimization feature selection for breast cancer recurrence prediction. IEEE Access 6:29637–29647. https://doi.org/10.1109/ACCESS.2018.2843443CrossRef

25.

Dorigo M, Gambardella LM (1997) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evol Comput 1(1):53–66. https://doi.org/10.1109/4235.585892CrossRef

26.

Fallahzadeh O, Dehghani-Bidgoli Z, Assarian M (2018) Raman spectral feature selection using ant colony optimization for breast cancer diagnosis. Lasers Med Sci 33(8):1799–1806. https://doi.org/10.1007/s10103-018-2544-3CrossRefPubMed

27.

Chu S-C, Tsai P-W, Pan J-S (2006) Cat swarm optimization. In PRICAI 2006: Trends in Artificial Intelligence: 9th Pacific Rim International Conference on Artificial Intelligence Guilin, China, August 7-11, 2006 Proceedings 9. Guilin, China Springer. 854–858. https://doi.org/10.1007/978-3-540-36668-3_94

28.

Wang SH, Lv YD (2018) Alcoholism detection by data augmentation and convolutional neural network with stochastic pooling. J Med Syst 42(2):1–11. https://doi.org/10.1007/s10916-017-0845-x

29.

Kumar P, Hati AS (2021) Transfer learning-based deep CNN model for multiple faults detection in SCIM. Neural Comput Appl 33(22):15851–15862. https://doi.org/10.1007/s00521-021-06205-1CrossRef

30.

Wang S-H (2018) Polarimetric synthetic aperture radar image segmentation by convolutional neural network using graphical processing units. J Real-Time Image Proc 15(3):631–642. https://doi.org/10.1007/s11554-017-0717-0CrossRef

31.

Desai M, Shah M (2021) An anatomization on breast cancer detection and diagnosis employing multi-layer perceptron neural network (MLP) and Convolutional neural network (CNN). Clinical eHealth 4:1–11. https://doi.org/10.1016/j.ceh.2020.11.002CrossRef

32.

Davoudi K, Thulasiraman P (2021) Evolving convolutional neural network parameters through the genetic algorithm for the breast cancer classification problem. SIMULATION 97(8):511–527. https://doi.org/10.1177/0037549721996031CrossRefPubMedPubMedCentral

33.

Harish H, Bharathi DS, Pratibha M, Holla D, Ashwini KB, Keerthana KR et al (2022) Particle swarm optimization for predicting breast cancer. In 2022 International conference on knowledge engineering and communication systems (ICKES). Chickballapur, India. 1–5. https://doi.org/10.1109/ICKECS56523.2022.10060690

34.

Lin R-H, Kujabi BK, Chuang C-L, Lin C-S, Chiu C-J (2022) Application of Deep Learning to Construct Breast Cancer Diagnosis Model. Appl Sci 12:1957. https://doi.org/10.3390/app12041957CrossRef

35.

Saha SK, Ghoshal SP, Kar R, Mandal D (2013) Cat Swarm Optimization algorithm for optimal linear phase FIR filter design. ISA Trans 52(6):781–794. https://doi.org/10.1016/j.isatra.2013.07.009CrossRefPubMed

36.

Yan D, Cao H, Yu Y, Wang Y, Yu X (2020) Single-objective/multiobjective cat swarm optimization clustering analysis for data partition. IEEE Trans Autom Sci Eng 17(3):1633–1646. https://doi.org/10.1109/TASE.2020.2969485CrossRef

37.

Du Y, Wang JL, Lei L (2019) Multi-objective scheduling of cloud manufacturing resources through the integration of cat swarm optimization and firefly algorithm. Production Engineering Institute (PEI). Fac Mech Eng 14(3):333–342. https://doi.org/10.14743/APEM2019.3.331CrossRef

38.

Mangalampalli S, Swain SK, Mangalampalli VK (2022) Multi objective task scheduling in cloud computing using cat swarm optimization algorithm. Arab J Sci Eng 47(2):1821–1830. https://doi.org/10.1007/s13369-021-06076-7CrossRef

39.

Zhang Y-D (2018) Cat Swarm Optimization applied to alcohol use disorder identification. Multimed Tools Appl 77(17):22875–22896CrossRef

40.

Wang S-H (2017) Facial emotion recognition via discrete wavelet transform, principal component analysis, and cat swarm optimization. In intelligence science and big data engineering. Springer International Publishing, Cham 10559:203–214. https://doi.org/10.1007/978-3-319-67777-4_18

41.

Ahmed AM, Rashid TA, Saeed SAM (2020) Cat swarm optimization algorithm: a survey and performance evaluation. Comput Intell Neurosci 2020:4854895. https://doi.org/10.1155/2020/4854895CrossRefPubMedPubMedCentral

42.

Kingma DP, Ba J (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980. https://doi.org/10.48550/arXiv.1412.6980

43.

Neunhoeffer M, Sternberg S (2018) How Cross-Validation Can Go Wrong and What to Do About It. Polit Anal 27(1):101–106. https://doi.org/10.1017/pan.2018.39CrossRef

44.

Rao RB, Fung G, Rosales R (2008) On the dangers of cross-validation. An experimental evaluation. In: Proceedings of the 2008 SIAM international conference on data mining (SDM), society for industrial and applied mathematics, Atlanta 588–596. https://doi.org/10.1137/1.9781611972788.5

45.

Jiang P, Chen J (2016) Displacement prediction of landslide based on generalized regression neural networks with K-fold cross-validation. Neurocomputing 198(1):40–47. https://doi.org/10.1016/j.neucom.2015.08.118CrossRef

46.

Wang W, Pei Y, Wang SH, Gorrz JM, Zhang YD (2023) PSTCNN: Explainable COVID-19 diagnosis using PSO-guided self-tuning CNN. Biocell 47(2):373–384. https://doi.org/10.32604/biocell.2021.0xxxCrossRefPubMed

47.

Balaji P, Muniasamy V, Bilfaqih SM, Muniasamy A, Tharanidharan S et al (2023) Chimp optimization algorithm Influenced Type-2 intuitionistic fuzzy C-Means clustering-based breast cancer detection system. Cancers 15(4):1131CrossRefPubMedPubMedCentral

48.

Ding K, Xu Z, Tong H, Liu H (2022) Data augmentation for deep graph learning: A survey. Assoc Comput Mach Special Interes Group Knowl Disc Data Explor Newsletter 24(2):61–77. https://doi.org/10.1145/3575637.3575646CrossRef

49.

Ding K, Xu Z, Tong H, Liu H (2022) Data augmentation for deep graph learning: A survey. ACM SIGKDD Explorations Newsl 24(2):61–77CrossRef

50.

Nguyen E (2018) Breast cancer detection via Hu moment invariant and feedforward neural network. AIP conference proceedings 1954:030014. https://doi.org/10.1063/1.5033394

51.

Guo Z-W (2018) Breast cancer detection via wavelet energy and support vector machine. In 27th IEEE international conference on robot and human interactive communication (ROMAN). Nanjing, China IEEE. 758–763. https://doi.org/10.1109/ROMAN.2018.8525650

52.

Suresh A, Udendhran R, Balamurgan M (2020) Hybridized neural network and decision tree based classifier for prognostic decision making in breast cancers. Soft Comput 24(11):7947–7953. https://doi.org/10.1007/s00500-019-04066-4CrossRef

53.

Khan MB, Saha PS, Shahrior R (2021) Feasible Detection of Breast Cancer Metastasis using a CNN-based Deep Learning Model. In International conference on electronics, communications and information technology (ICECIT). Khulna Univ, Elect & Commun Engn Discipline, ELECTR NETWORK IEEE. 1–4. https://doi.org/10.1109/icecit54077.2021.9641195

54.

Yun CH, Eom B, Park S, Kim C, Kim D et al (2023) A study on the effectiveness of deep learning-based anomaly detection methods for breast ultrasonography. Sensors 23(5):2864. https://doi.org/10.3390/s23052864ADSCrossRefPubMedPubMedCentral

Title: CSO-CNN: Cat Swarm Optimization-guided Convolutional Neural Network for Mobile Detection of Breast Cancer
Authors: Xiaoyan Jiang
Zuojin Hu
Zhaozhao Xu
Publication date: 05-03-2024
Publisher: Springer US
Published in: Mobile Networks and Applications
Print ISSN: 1383-469X
Electronic ISSN: 1572-8153
DOI: https://doi.org/10.1007/s11036-024-02298-9

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