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Computer Vision in Deep Learning for the Detection of Cancer and its Treatment


Affiliations
1 Department of Computer Science, Dr V S Krishna Govt Degree and PG College (Autonomous), A. U. TDR – HUB, Visakhapatnam, Andhra Pradesh, India
2 Dept of Computer Science and Engineering, Guru Nanak Institute of Technology, Hyderabad, Telangana, India
3 Anheuser-Busch InBev, Bangalore, Karnataka, India
4 Masters in Technology Management, University of Illinois urbana, Champaign, United States
 

Computer vision (CV) is an effective mechanism that helps the computer to see pictorial stimuli from pointing out the edges to having a comprehensive understanding of the complete scenario. In this saga, Deep Learning (DL) has evolved as a crucial part of CV to process data exploiting multi-layered complex structures or layers made of multiple nonlinear alterations. This particular research shows the implementation of DL in the proper diagnosis of cancer and seeking a suitable solution to the disease. DL is an integral part of CV considering a multimodal discriminative model to conduct a diagnosis of diseases, clinical predictions, prognostics, and a combination of such activities. The study upholds the relevance of SSD in having single-shot images with high-resolution pixels to have the images to identify and diagnose the disease. The mechanism leads to early detection of cancer and if the disease gets detected earlier, it can seek a formidable solution, though there are challenges like an alignment of hardware with the CV software, and lack of training of the staff, still DL has the potentiality to create a significant impact on cancer treatment.

Keywords

Computer Vision (CV), Deep Learning (DL), Single Shot Detector (SSD), Cancer Detection Algorithms.
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  • . Suzuki, K. (2017). Overview of deep learning in medical imaging. Radiological physics and technology, 10(3), 257-273.

  • . Kim, M., Yun, J., Cho, Y., Shin, K., Jang, R., Bae, H., & Kim, N. (2019). Deep learning in medical imaging. Neurospine, 16(4), 657.

  • . Walsh, P. F. (2020). Improving ‘Five Eyes’ health security intelligence capabilities: leadership and governance challenges. Intelligence and National Security, 35(4), 586-602.

  • . Messum, D., Wilkes, L., Peters, C., & Jackson, D. (2017). Senior Managers’ and Recent Graduates’ Perceptions of Employability Skills for Health Services Management. Asia-Pacific Journal of Cooperative Education, 18(2), 115-128.

  • . Lu, L., Wang, X., Carneiro, G., & Yang, L. (2019). Deep learning and convolutional neural networks for medical imaging and clinical informatics. New Jersey: Springer International Publishing.

  • . Ken-Opurum, J., Darbishire, L., Miller, D., & Savaiano, D. (2020). Assessing Rural Health Coalitions Using the Public Health Logic Model: A Systematic Review. American Journal of Preventive Medicine, 58(6), 864-878.

  • . Y. Jahnavi and Y. Radhika, "Hot topic extraction based on frequency, position, scattering and topical weight for time sliced news documents," 2013 15th International Conference on Advanced Computing Technologies (ICACT), Rajampet, India, 2013, pp. 1-6, doi: 10.1109/ICACT.2013.6710495.

  • . Jahnavi, Y. and Radhika, Y., FPST: a new term weighting algorithm for long running and short lived events, Int. J. Data Analysis Techniques and Strategies, Vol. 7, No. 4, pp.366–383, (2015).

  • . Jahnavi, Y., Statistical data mining technique for salient feature extraction, International Journal of Intelligent Systems Technologies and Applications, Vol. 18, No. 4, pp. 353-376, (2019).

  • . Jahnavi Y, “A Cogitate Study on Text Mining”, International Journal of Engineering and Advanced Technology, Vol. 1, No. 6, pp. 189 -196, (2012).

  • . Jahnavi Yeturu, Analysis of weather data using various regression algorithms, Int. J. Data Science (Inderscience Publishers), Vol. 4, No. 2, pp. 117- 141, (2019).

  • . Jahnavi, Y., Elango, P., Raja, S.P. et al. A new algorithm for time series prediction using machine learning models. Evol. Intel. 16, 1449–1460 (2023). https://doi.org/10.1007/s12065-022-00710-5.

  • . Jahnavi, Yeturu, et al. "A Novel Ensemble Stacking Classification of Genetic Variations Using Machine Learning Algorithms." International Journal of Image and Graphics, Vol. 23, No. 02 (2023).

  • . Jahnavi, Yeturu, et al. "A Novel Processing of Scalable Web Log Data Using Map Reduce Framework." Computer Vision and Robotics: Proceedings of CVR 2022. Singapore: Springer Nature Singapore, 2023. 15-25.

  • . Nagendra, K. V., Y. Jahnavi, and N. Haritha. "A survey on support vector machines and artificial neural network in rainfall forecasting." Int. J. Future Revolut. Comput. Sci. Commun. Eng 3 (2017): 20- 24.

  • . Sukanya et al., “Country location classification on tweets,” Indian J. Public Health Res. Dev. 10(5), 890–898 (2019).

  • . Bhargav K, Asiff SK, Jahnavi Y (2019) An extensive study for the development of web pages. Indian J Public Health Res Dev 10(5).

  • . Vijaya, U., Y. Jahnavi, and G. Subba Rao. "Community-Based Health Service for Lexis Gap in Online Health Seekers."

  • . Jahnavi, Y., V. R. Balasaraswathi, and P. Nagendra Kumar. "Model Building and Heuristic Evaluation of Various Machine Learning Classifiers." International Conference on Sustainable and Innovative Solutions for Current Challenges in Engineering & Technology. Singapore: Springer Nature Singapore, 2022.

  • . Jahnavi, Y., et al. "Prediction and Evaluation of Cancer Using Machine Learning Techniques." International Conference on Sustainable and Innovative Solutions for Current Challenges in Engineering & Technology. Singapore: Springer Nature Singapore, 2022.

  • . Lakshmi M, Sukeerthi K, Jahnavi Y (2019) Security health monitoring and attestation of virtual machines in cloud computing. Indian J Publ Health Res Dev 10(5).

  • . Puttagunta, M., & Ravi, S. (2021). Medical image analysis based on deep learning approach. Multimed Tools Appl, 80, 24365–24398.

  • . Esteva, A., Chou, K., & Yeung, S. (2021). Deep learning-enabled medical computer vision. npj Digit. Med, 4, 5.

  • . Shmerling, R. H. (2019, December 10). Can vaping damage your lungs? What we do (and don’t) know - Harvard Health Blog. Retrieved November 30, 2020, from Harvard Health Blog: https://www.health.harvard.edu/blog/can-vapingdamage- your-lungs-what-we-do-and-dont-know- 2019090417734.

  • . Cai, L., Gao, J., & Zhao, D. (2020). A review of the application of deep learning in medical image classification and segmentation. ATM: Annals of Translational Medicine, 8(11), 11-17.

  • . Yi, X., Walia, E., & Babyn, P. (2019). Generative adversarial network in medical imaging: A review. Medical image analysis, 58, 101552.

  • . Sahasrabudhe, M. (2020). Unsupervised and weakly supervised deep learning methods for computer vision and medical imaging. Paris: Univeristé Paris-Saclay.

  • . Domingues, I., Sampaio, I., Duarte, H., Santos, J., & Abreu, P. (2019). Computer vision in esophageal cancer: a literature review. IEEE Access, 7, 103080-103094.

  • . Yoo, S., Gujrathi, I., Haider, M., & Khalvati, F. (2019). Prostate cancer detection using deep convolutional neural networks. Scientific reports, 9(1), 1-10.

  • . Y. Jahnavi, A New Term Weighting Algorithm for Identifying Salient Events (LAP LAMBERT Academic Publishing, 2018)

  • . Y. Jahnavi, Data Classification using Waikato Environment for Knowledge Analysis (LAP LAMBERT Academic Publishing, 2019)


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  • Computer Vision in Deep Learning for the Detection of Cancer and its Treatment

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Authors

Y. Jahnavi
Department of Computer Science, Dr V S Krishna Govt Degree and PG College (Autonomous), A. U. TDR – HUB, Visakhapatnam, Andhra Pradesh, India
P. Anusha
Dept of Computer Science and Engineering, Guru Nanak Institute of Technology, Hyderabad, Telangana, India
Mudit Mathur
Anheuser-Busch InBev, Bangalore, Karnataka, India
Vidisha Tiwari
Masters in Technology Management, University of Illinois urbana, Champaign, United States

Abstract


Computer vision (CV) is an effective mechanism that helps the computer to see pictorial stimuli from pointing out the edges to having a comprehensive understanding of the complete scenario. In this saga, Deep Learning (DL) has evolved as a crucial part of CV to process data exploiting multi-layered complex structures or layers made of multiple nonlinear alterations. This particular research shows the implementation of DL in the proper diagnosis of cancer and seeking a suitable solution to the disease. DL is an integral part of CV considering a multimodal discriminative model to conduct a diagnosis of diseases, clinical predictions, prognostics, and a combination of such activities. The study upholds the relevance of SSD in having single-shot images with high-resolution pixels to have the images to identify and diagnose the disease. The mechanism leads to early detection of cancer and if the disease gets detected earlier, it can seek a formidable solution, though there are challenges like an alignment of hardware with the CV software, and lack of training of the staff, still DL has the potentiality to create a significant impact on cancer treatment.

Keywords


Computer Vision (CV), Deep Learning (DL), Single Shot Detector (SSD), Cancer Detection Algorithms.

References