Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.1145/3542954.3542981acmotherconferencesArticle/Chapter ViewAbstractPublication PagesiccaConference Proceedingsconference-collections
research-article

An Expedition to Intelligent Diagnosis of Bone Cancer and It's Direction from Capsule Network

Published: 11 August 2022 Publication History

Abstract

Although Early diagnosis is the lifesaving strategy and superfluous appurtenant to bone cancer. Pathologists, however, find it incommodious to detect bone cancer early due to cellular heterogeneity in Computerized Tomography (CT) and Magnetic Resonance Imaging (MRI) images. In this regard, many image processing techniques can assist doctors in classifying cancer and non-cancer samples intelligently. Among various image processing and data mining techniques, Deep Learning outperforms other techniques in image classification applications. However, popular Deep Learning architectures (i.e. AlexNet, VGG-16, Inception-Net, ResNet etc.) still have some serious drawbacks where higher level layers combine lower level features and then construct result regardless of order and spatial relations among the image features. Moreover, it exhibits less accuracy with small dataset in bone cancer classification. This research aims to classify bone cancer from MRI and CT images with high accuracy and less training data as there is scarcity of real bone cancer data. Therefore, we propose an approach combining with Capsule Network which satisfies the research aim. The qualitative and numerical analysis exhibits that, proposed framework provides upper horizontal accuracy than other Deep Learning techniques with the percentage of 95.26 while detecting the bone cancer.

Supplementary Material

Presentation slides (p178-hasan-supplement.pptx)

References

[1]
A.I. Baba (2008). Comparative Oncology Review. Lucarari Stiinifice Medicina Vetenerara. https://www.usab-tm.ro/vol8MV/14_vol8.pdf
[2]
Andrew G.G, M.D., Joseph F.F., Jr., ”Epidemiology of Bone Cancer,” Journal of the National Cancer Institute (JNCI), 1970, Vol. 44, Issue 1, pp.187-199. https://doi.org/10.1093/jnci/44.1.187
[3]
Rajeshwar Dass, M.D., Priyanka, Swapna Devi, ”Image Segmentation Techniques,” Indian Journal of Extra-Corporeal Technology (IJECT), 2012, Vol. 3, Issue 1, pp. 66-70. http://www.iject.org/vol3issue1/rajeshwar.pdf
[4]
Eftekhar Hossain, M. Anisur Rahman., Priyanka, Swapna Devi, ” Bone Cancer Detection & Classification Using Fuzzy Clustering & Neuro Fuzzy Classifier,” 4th International Conference on IEEE, 2018, pp. 541-546. https://doi.org/10.1109/CEEICT.2018.8628164
[5]
J Madhuri Avula, N. P. Lakkakula, Murali P. R, ” Bone Cancer Detection from MRI Scan Imagery Using Mean Pixel Intensity,” 8th International Conference on IEEE, 2014, pp. 141-146. https://doi.org/10.1109/AMS.2014.36
[6]
Keiron O Shea, Ryan Nash, “An Introduction to Convolutional Neural Networks”, arXiv, 2015, pp. 1-11. https://doi.org/10.48550/arXiv.1511.08458
[7]
James Phil Kim (2017). Convolutional Neural Network. Appress, Berkelry, CA. [8] M. A. Anupama, V. Sowmya and K. P. Soman, "Breast Cancer Classification using Capsule Network with Preprocessed Histology Images," 2019 International Conference on Communication and Signal Processing (ICCSP), 2019, pp. 0143-0147. https://doi.org/10.1109/ICCSP.2019.8698043
[8]
B. Wimpy and S. Suyanto, "Classification of Cervical Type Image Using Capsule Networks," 2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), 2019, pp. 34-37. https://doi.org/10.1109/ISRITI48646.2019.9034663
[9]
A. Gulli, S. Paul (2017). Deep learning with Keras: Implementation deep learning models and neural networks with the power of python. Lucarari Stiinifice Medicina Vetenerara. Packt,
[10]
Khushboo Munir, Hassan Elahi, ”Cancer Diagnosis Using Deep Learning: A Bibliographic Reveiw,” Multidisciplinary Digital Publishing Institute (MDPI), 2019, Vol. 11, Issue 9, pp. 1235-1271. https://doi.org/10.3390/cancers11091235
[11]
Shanchen Pang, Fan Meng,”VGG16-T: A Novel Deep Convolutional Neural Network with Boosting to Identify Pathological Type of Lung Cancer in Early Stage by CT Images,” International Journal of Computational Intelligence Systems, 2020, Vol. 13(1), pp. 771-780. https://doi.org/10.2991/ijcis.d.200608.001
[12]
Boris Milovic “Prediction and Decision Making in Health Care using Data Mining” International Journal of Public Health Science, December 2012, Vol. 1, No. 2, pp. 69-78. https://www.academia.edu/download/33572666/1380-4163-1-PB_(1).pdf
[13]
Shomona Gracia Jacob “Data Mining in Clinical Data Sets: A. Review” International Journals of Applied Information System (IJAIS),New York, USA, December 2012, Volume 4-No.6. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.401.7871&rep=rep1&type=pdf
[14]
Sasikala and Vasanthakumar, “Breast Cancer - Classification And Analysis Using Different Scanned Images,” International Journal of Image Processing and Visual Communication, 2012, pp. 1-7. 39.
[15]
Dina Aboul Dahab, Samy S,A. Ghoniemy and Gamal M. Selim, “Automated Brain Tumor Detection and Identification Using Image Processing and Probabilistic Neural Network Techniques,” International Journal of Image Processing and Visual Communication, 2012, pp. 1-8. https://www.academia.edu/download/31780866/IJIPVCV1I201.pdf
[16]
Shilpa Kamdi and R.K.Krishna, “Image Segmentation and Region Growing Algorithm,” International Journal of Computer Technology and Electronics Engineering, 2012, pp. 103-107.
[17]
W.-T. Tseng, W.-F. Chiang, S.-Y. Liu, J. Roan, and C.-N. Lin, "The application of data mining techniques to oral cancer prognosis," Journal of medical systems, 2015, Vol. 39, Issue 5, pp. 59-65. https://link.springer.com/article/10.1007/s10916-015-0241-3
[18]
P.Ramachandran, N.Girija, T.Bhuvaneswari, " Early Detection and Prevention of Cancer using Data Mining Techniques," International Journal of Computer Applications, 2014, Vol. 97, Issue 13, pp. 48-53. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.681.6233&rep=rep1&type=pdf
[19]
S.H. Wang, J.D. Sun, I. Mehmood, C.C. Pan, Y. Chen, Y.D. Zhang, ”Cerebral micro-bleeding identification based on a nine-layer convolutional neural network with stochastic pooling,” Concurr. Comput. Pract. 2020. https://doi.org/10.1002/cpe.5130
[20]
Eapen, A. G. (2004). Application of Data mining in Medical Applications. Ontario, Canada, 2004: University of Waterloo. http://hdl.handle.net/10012/772
[21]
Othmani, A., Taleb, A., Abdelkawy, H. & Hadid, A. (2020). Age estimation from faces using deep learning: A comparative analysis. Computer Vision and Image Understanding, vol 196. https://doi.org/10.1016/j.cviu.2020.102961
[22]
Kaur, Prabhjot, ” Empirical Analysis of Cervical and Breast Cancer Prediction Systems using Classification,” International Journal of Education and Management Engineering, Hong Kong, May 2019, Vol. 9, Issue 3, pp. 1-15. https://doi.org/10.5815/ijeme.2019.03.01
[23]
R.M. Chandrasekar, V. Palaniammal, ” Performance and Evaluation of Data Mining Techniques in Cancer Diagnosis,” IOSR Journal of Computer Engineering (IOSR-JCE), December 2013, Vol. 15, Issue 5, pp. 39-44. https://www.academia.edu/download/33742142/G01553944.pdf
[24]
Vikas Chaurasia, Saurabh Pal, ” Performance analysis of data mining algorithms for diagnosis and prediction of heart and breast cancer disease,” ISSN, May 2014, Vol. 3, Issue 8, pp. 1-13. https://www.academia.edu/download/33939005/771.pdf
[25]
Ahmad,” Using Three Machine Learning Techniques for Predicting Breast Cancer Recurrence”, J Health Med Inform 2013, Vol. 4, Issue 2, pp. 1-3. https://www.academia.edu/download/35844306/using-three-machine-learning-techniques-for-predicting-breast-cancer-2157-7420.1000124.pdf
[26]
Kristina Bliznakova, Nikola Kolev, Zhivko Bliznakov, Ivan Buliev, Anton Tonev, Elitsa Encheva and Krasimir Ivanov, “Image Processing Tool Promoting Decision-Making in Liver Surgery of Patients with Chronic Kidney Disease,” Journal of Software Engineering and Applications, 2014, pp. 118-127. https://doi.org/10.4236/jsea.2014.72013
[27]
Jie Wu1, Skip Poehlman, Michael D. Noseworthy Markad and V. Kamath, “Texture feature based automated seeded region growing in abdominal MRI segmentation,” Journal of Biomedical Science and Engineering, 2009, pp. 1-8. https://doi.org/10.1109/BMEI.2008.352
[28]
N. Mohd Saad, S.A.R. Abu-Bakar, Sobri Muda, M. Mokji and A.R. Abdullah, “Automated Region Growing for Segmentation of Brain Lesion in Diffusion-weighted MRI,” IAENG International Journal of Computer Science, 2012, pp.155. http://www.iaeng.org/IJCS/issues_v39/issue_2/IJCS_39_2_03.pdf
[29]
M. S. Hosseini and M. Zekri, “Review of medical image classification using the adaptive neuro-fuzzy inference system,” Journal of medical signals and sensors, vol. 2, no. 1, p. 49, 2012. https:// .ncbi.nlm.nih.gov/23493054
[30]
N. R. Pal and S. K. Pal, “A review on image segmentation techniques,” Pattern recognition, vol. 26, no. 9, pp. 1277–1294, 1993. https://doi.org/10.1016/0031-3203(93)90135-J
[31]
N. Ramkumar, S. Prakash, S. A. Kumar and K. Sangeetha, "Prediction of liver cancer using Conditional probability Bayes theorem," 2017 International Conference on Computer Communication and Informatics (ICCCI), 2017, pp. 1-5. https://doi.org/10.1109/ICCCI.2017.8117752
[32]
P. Rajeswari and Sophia “Human Liver Cancer Classification using Microarray Gene Expression Data” November 2011, International Journal of Computer Applications (0975 – 8887) Volume 34– No.6. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.259.2512&rep=rep1&type=pdf
[33]
A. Addeh, H. Demirel, and P. Zarbakhsh, “Early detection of breast cancer using optimized anfis and features selection,” in Computational Intelligence and Communication Networks (CICN), 2017 9th International Conference on. IEEE, 2017, pp. 39–42. https://doi.org/10.1109/CICN.2017.8319352
[34]
K. Machhale, H. B. Nandpuru, V. Kapur, and L. Kosta, “Mri brain cancer classification using hybrid classifier (svm-knn),” in Industrial Instrumentation and Control (ICIC), 2015 International Conference on. IEEE, 2015, pp. 60–65. https://doi.org/10.1109/IIC.2015.7150592
[35]
K. D. Mistry and B. J. Talati, “Integrated approach for bone tumor detection from mri scan imagery,” in Signal and Information Processing (IConSIP), International Conference on. IEEE, 2016, pp. 1–5. https://doi.org/10.1109/ICONSIP.2016.7857471
[36]
H. D. Dorfman and B. Czerniak, “Bone cancers,” Cancer, vol. 75, no. S1, pp. 203–210, 1995.
[37]
He, K., Zhang, X., Ren S. and Sun, J. (2016). Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp 770-778. https://openaccess.thecvf.com/content_cvpr_2016/papers/He_Deep_Residual_Learning_CVPR_2016_paper.pdf
[38]
Juliet R Rajan and Jefrin J Prakash, “Early Diagnosis of Lung Cancer using a Mining Tool,” International Journal of Emerging Trends and Technology in Computer Science, 2013.
[39]
Samir Kumar Bandyopadhyay and Tuhin Utsab Paul, “Segmentation of Brain Tumour from MRI ImageAnalysis of K-means and DBSCAN Clustering,” International Journal of Research in Engineering and Science, 2013, pp.77-98. 41. http://ijres.org/papers/v1-i1/H0114857.pdf
[40]
Samir Kumar Bandyopadhyay and Tuhin Utsab Paul, “Segmentation of Brain Tumour from MRI ImageAnalysis of K-means and DBSCAN Clustering,” International Journal of Research in Engineering and Science, 2013, pp.77-98. 41. http://www.ijres.org/papers/v1-i1/H0114857.pdf

Cited By

View all
  • (2023)A Deep Evaluation of Digital Image based Bone Cancer Prediction using Modified Machine Learning Strategy2023 9th International Conference on Smart Structures and Systems (ICSSS)10.1109/ICSSS58085.2023.10407146(1-6)Online publication date: 23-Nov-2023

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
ICCA '22: Proceedings of the 2nd International Conference on Computing Advancements
March 2022
543 pages
ISBN:9781450397346
DOI:10.1145/3542954
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 August 2022

Permissions

Request permissions for this article.

Check for updates

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Conference

ICCA 2022

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)20
  • Downloads (Last 6 weeks)2
Reflects downloads up to 28 Dec 2024

Other Metrics

Citations

Cited By

View all
  • (2023)A Deep Evaluation of Digital Image based Bone Cancer Prediction using Modified Machine Learning Strategy2023 9th International Conference on Smart Structures and Systems (ICSSS)10.1109/ICSSS58085.2023.10407146(1-6)Online publication date: 23-Nov-2023

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media