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Fusion: Practice and Applications
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Title

Breast Cancer Detection Using Deep Learning and Feature Decision Level Fusion

  Surinder Kaur 1 * ,   Javalkar Dinesh Kumar 2 ,   Gopal Chaudhary 3 ,   Manju Khari 4

1  Lingaya’s Vidyapeeth, Haryana, India and Bharati Vidyapeeth’s College of Engineering, New Delhi, India
    (kaur.surinder@bharatividyapeeth.edu)

2  Lingaya’s Vidyapeeth, Haryana, India
    (dineshjavalkar@lingayasvidyapeeth.edu.in)

3  Bharati Vidyapeeth’s College of Engineering, Delhi, India
    (gopal.bvcoe@bharatividyapeeth.edu)

4  Jawaharlal Nehru University, New Delhi, India
    (manjukhari@mail.jnu.ac.in)


Doi   :   https://doi.org/10.54216/FPA.080105

Received: March 28, 2022 Accepted: August 21, 2022

Abstract :

Among women, breast cancer has a high incidence and high fatality rate. Due to a lack of early detection facilities and barriers to accessing technological improvements in battling this illness, mortality rates are disproportionately greater in underdeveloped countries. Biopsies done by trained pathologists are the only certain approach to diagnosing cancer. With the use of computer-aided diagnostic algorithms, pathologists may improve their efficiency, objectivity, and consistency in making diagnoses. A key goal of this research is to create an accurate automated system for diagnosing breast cancer that can function in the current clinical setting. In this work, we offer an algorithm for the identification of breast cancer that uses asymmetric analysis as the basic choice and decision-level fusion. Fusion of local nuclei features extracted using convolutional neural network (CNN) models pre-trained on the database constitutes the picture feature representation. The dataset is accessible for public use, and the results are evaluated by running 25 random trials with an 80%-20% split between train and test. Overall, the suggested framework was 86%. The proposed framework is shown to outperform numerous current methods and to provide results on par with the state-of-the-art techniques without requiring extensive computing resources. Breast cancer detection from histological pictures may be greatly aided by the use of this qualitative approach based on transfer learning.

Keywords :

Breast Cancer; Decision Level Fusion; Feature Fusion; Classifier Fusion; Deep Learning

References :

[1] Y. R. Parisky et al., “Efficacy of computerized infrared imaging analysis to evaluate mammographically

suspicious lesions,” American Journal of Roentgenology, vol. 180, no. 1, pp. 263–270, 2003.

[2] D. A. Kennedy, T. Lee, and D. Seely, “A comparative review of thermography as a breast cancer

screening technique,” Integrative cancer therapies, vol. 8, no. 1, pp. 9–16, 2009.

[3] K. Holland, C. H. van Gils, R. M. Mann, and N. Karssemeijer, “Quantification of masking risk in

screening mammography with volumetric breast density maps,” Breast cancer research and treatment,

vol. 162, no. 3, pp. 541–548, 2017.

[4] A. P. Brady, “Error and discrepancy in radiology: inevitable or avoidable?,” Insights into imaging, vol.

8, no. 1, pp. 171–182, 2017.

[5] P. Olliaro and E. Torreele, “Managing the risks of making the wrong diagnosis: First, do no harm,”

International Journal of Infectious Diseases, vol. 106, pp. 382–385, 2021.

[6] S. Z. Ramadan, “Methods used in computer-aided diagnosis for breast cancer detection using

mammograms: a review,” Journal of healthcare engineering, vol. 2020, 2020.

[7] E. Sorantin et al., “The augmented radiologist: Artificial intelligence in the practice of radiology,”

Pediatric Radiology, pp. 1–13, 2021.

[8] K. Sirinukunwattana, S. E. A. Raza, Y.-W. Tsang, D. R. J. Snead, I. A. Cree, and N. M. Rajpoot,

“Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer

histology images,” IEEE transactions on medical imaging, vol. 35, no. 5, pp. 1196–1206, 2016.

[9] A. Maier, C. Syben, T. Lasser, and C. Riess, “A gentle introduction to deep learning in medical image

processing,” Zeitschrift für Medizinische Physik, vol. 29, no. 2, pp. 86–101, 2019.

[10] J. H. Tan et al., “Automated segmentation of exudates, haemorrhages, microaneurysms using single

convolutional neural network,” Information sciences, vol. 420, pp. 66–76, 2017.

[11] S. Ali and A. Madabhushi, “An integrated region-, boundary-, shape-based active contour for multiple

object overlap resolution in histological imagery,” IEEE transactions on medical imaging, vol. 31, no. 7,

pp. 1448–1460, 2012.

[12] M. Salvi, F. Molinari, N. Dogliani, and M. Bosco, “Automatic discrimination of neoplastic epithelium

and stromal response in breast carcinoma,” Computers in Biology and Medicine, vol. 110, pp. 8–14,

2019.

[13] H. Sharma et al., “A multi-resolution approach for combining visual information using nuclei

segmentation and classification in histopathological images.,” in VISAPP (3), 2015, pp. 37–46.

[14] M. Talo, U. B. Baloglu, Ö. Yıldırım, and U. R. Acharya, “Application of deep transfer learning for

automated brain abnormality classification using MR images,” Cognitive Systems Research, vol. 54, pp.

176–188, 2019.

[15] U. K. Lopes and J. F. Valiati, “Pre-trained convolutional neural networks as feature extractors for

tuberculosis detection,” Computers in biology and medicine, vol. 89, pp. 135–143, 2017.

[16] O. A. B. Penatti, K. Nogueira, and J. A. Dos Santos, “Do deep features generalize from everyday objects

to remote sensing and aerial scenes domains?,” in Proceedings of the IEEE conference on computer

vision and pattern recognition workshops, 2015, pp. 44–51.

[17] K. Gopalakrishnan, S. K. Khaitan, A. Choudhary, and A. Agrawal, “Deep convolutional neural networks

with transfer learning for computer vision-based data-driven pavement distress detection,” Construction

and building materials, vol. 157, pp. 322–330, 2017.

[18] M. Oberlaender, V. J. Dercksen, R. Egger, M. Gensel, B. Sakmann, and H.-C. Hege, “Automated threedimensional

detection and counting of neuron somata,” Journal of neuroscience methods, vol. 180, no. 1,

pp. 147–160, 2009.

[19] M. Kowal, P. Filipczuk, A. Obuchowicz, J. Korbicz, and R. Monczak, “Computer-aided diagnosis of

breast cancer based on fine needle biopsy microscopic images,” Computers in biology and medicine, vol.

43, no. 10, pp. 1563–1572, 2013.

[20] P. Filipczuk, T. Fevens, A. Krzyżak, and R. Monczak, “Computer-aided breast cancer diagnosis based

on the analysis of cytological images of fine needle biopsies,” IEEE transactions on medical imaging,

vol. 32, no. 12, pp. 2169–2178, 2013.


Cite this Article as :
Style #
MLA Surinder Kaur , Javalkar Dinesh Kumar, Gopal Chaudhary, Manju Khari. "Breast Cancer Detection Using Deep Learning and Feature Decision Level Fusion." Fusion: Practice and Applications, Vol. 8, No. 1, 2022 ,PP. 50-59 (Doi   :  https://doi.org/10.54216/FPA.080105)
APA Surinder Kaur , Javalkar Dinesh Kumar, Gopal Chaudhary, Manju Khari. (2022). Breast Cancer Detection Using Deep Learning and Feature Decision Level Fusion. Journal of Fusion: Practice and Applications, 8 ( 1 ), 50-59 (Doi   :  https://doi.org/10.54216/FPA.080105)
Chicago Surinder Kaur , Javalkar Dinesh Kumar, Gopal Chaudhary, Manju Khari. "Breast Cancer Detection Using Deep Learning and Feature Decision Level Fusion." Journal of Fusion: Practice and Applications, 8 no. 1 (2022): 50-59 (Doi   :  https://doi.org/10.54216/FPA.080105)
Harvard Surinder Kaur , Javalkar Dinesh Kumar, Gopal Chaudhary, Manju Khari. (2022). Breast Cancer Detection Using Deep Learning and Feature Decision Level Fusion. Journal of Fusion: Practice and Applications, 8 ( 1 ), 50-59 (Doi   :  https://doi.org/10.54216/FPA.080105)
Vancouver Surinder Kaur , Javalkar Dinesh Kumar, Gopal Chaudhary, Manju Khari. Breast Cancer Detection Using Deep Learning and Feature Decision Level Fusion. Journal of Fusion: Practice and Applications, (2022); 8 ( 1 ): 50-59 (Doi   :  https://doi.org/10.54216/FPA.080105)
IEEE Surinder Kaur, Javalkar Dinesh Kumar, Gopal Chaudhary, Manju Khari, Breast Cancer Detection Using Deep Learning and Feature Decision Level Fusion, Journal of Fusion: Practice and Applications, Vol. 8 , No. 1 , (2022) : 50-59 (Doi   :  https://doi.org/10.54216/FPA.080105)