The Cancer Detection using Deep Learning: A Survey

Cancer Detection using Deep Learning: A Survey


  • Ibrahim Shuaib Odunlami Federal University Lokoja
  • Dr. Edgar Osaghae Federal University Lokoja
  • Dr. Basaky Frederick Federal University Lokoja


Artificial intelligence, Neural Network, Computed Tomography, Machine Learning, Pancreatic cancer


Cancer refers to a group of diseases that are defined by abnormal cell proliferation and can spread to other parts of the body. Pancreatic cancer originates in the organ behind the lower stomach called the pancreas. The pancreas secretes digestive enzymes as well as hormones that help regulate sugar metabolism. Pancreas cancer is usually discovered late, spreads quickly, and has a poor prognosis. Cancer-specific symptoms may not appear until later stages, and there are no reliable screening techniques to identify high-risk patients. The manual method of checking whether a patient has Pancreatic cancer or not can be time-consuming and it can be tedious because it required experience in Radiologist interpretation; to avoid poor decisions by giving wrong diagnoses to patients. One of the leading causes of cancer death is pancreatic cancer; however, if found early enough, it can be treated. To solve this issue, an Artificial Neural Network (ANN) has been developed, trained, and tested using an abdominal Computed Tomography (CT) scan images dataset to detect pancreatic tumors by many authors. To detect the tumor, it employs image processing techniques of a convolution neural network to detect the tumorous area in the image after it has been pre-processed. It was observed that the technique gives better accuracy in tumor detection.



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Author Biographies

Ibrahim Shuaib Odunlami, Federal University Lokoja

Computer Science Department


Federal University Lokoja

Dr. Edgar Osaghae, Federal University Lokoja

Computer Science Department,

Associate Professor,

Federal University Lokoja

Dr. Basaky Frederick, Federal University Lokoja

Computer Science Department,

Senior Lecturer,

Federal University Lokoja


S. Boursi, B. Ben, F. Brian, G. Bruce, H. Kevin, et al. “Clinical prediction model to assess risk for pancreatic cancer among patients with pre-diabetes”. Oncol. suppl.e1 March, 2017.

P. Klein, S. Lindström, B. Mendelsohn, E. Steplowski, A. Arslan, et al. “An absolute risk model to identify individuals at elevated risk for pancreatic cancer in the general population". PLoS ONE. 8(9), September, 2013.

V. Dasale, D. Vijaykumar, T. Bakhale, S. Kadam, D. Somnath “Detection of lung Tumor in its early stages using Image processing Techniques”. (IJCST), 5(2) March, 2017.

Cooper GM. The Cell: A Molecular Approach. 2nd edition. Sunderland (MA): Sinauer Associates; 2000. The Development and Causes of Cancer. Available from: ttps://

Y. Nemlich, E. Greenberg, R. Ortenberg, M. Besser, L. Rivkin, et al. “MicroRNA-mediated loss of ADAR1 in metastatic melanoma promotes tumor growth”: J Clin Invest 123(6), June 2013

M. Hussain, M. Tabassum, P. Ansari, S. Gawas, N. Chowdhury “Lung cancer Detection Using Artificial Neural Network and Fuzzy Clustering.” IJARCCE, 4(3) March, 2015

J. McCarthy, K. Max, P. Hoffman, G. Alexander, O. Philip “Application of machine learning and high dimension visualization in cancer detection, diagnosis, and management.” Annals of New York Academy of Sciences, 239-262 May 2004

D. Jaswal, V. Sowmya, K. Soman “Image Classification Using Convolutional Neural Networks”: IJART, Volume 3, Issue 6, June-2014

X. Han, & Y. Li “The Application of Convolutional Network in handwritten Numeral Recognition.” IJD: Theory and application.4(3). 2015

K. Si, Y. Xue, X. Yu, X. Zhu, Q. Li, et al. “Fully end-to-end deep-learning-based diagnosis of pancreatic tumors.” Theranostics, 11(4) 2021

M. Andrew, K. Paul, C. Richard, J. Claire, G. Helen, et al. “Pancreatic cancer: A review of clinical diagnosis, epidemiology, treatment, and outcomes.” Baishideng Publishing Group Inc. 24(43):4846-4861 November 2018.

J. Chakraborty, M. Abhishek, G. Lior, A. Marc, L. Liana, et al. “CT radiomics to predict high-risk intraductal papillary mucinous neoplasms of the pancreas.” Med Phys. 45(11): 5019-29 November 2018.

Z. Zhu, Y. Xia, L. Xie, E. Fishman, L. Yuille, “Multi-scale coarse-to-fine segmentation for screening pancreatic ductal adenocarcinoma.” MICCAI, 2019; 11769: 3-12.

R. Wei, K. Lin, W. Yan, Y. Guo, Y. Wang, et al. “Computer-aided diagnosis of pancreas serous cystic neoplasms: a radiomics method on preoperative MDCT images.” Technol Cancer Res Treat. January 2019, 1(18).

T. Boers, Y. Hu, E. Gibson, D. Barratt, E. Bonmati, et al. “Interactive 3D U-net for the segmentation of the pancreas in computed tomography scans”, Phys Med Biol. 65(6) March 2020.

M. Wazir, H. Gregory, N. Bradley, F. James, J. Kimberly et al. “Pancreatic Cancer Prediction through an Artificial Neural Network.” Department of Therapeutic Radiology, Department of Internal Medicine, School of Medicine, Yale University, New Haven, CT, United States. Artif. Intell., May 2019 |

S. Jeenal, S. Surve, V. Turkar. “Pancreatic Tumor Detection Using Image Processing.” Fr. Conceicao Rodrigues College of Engineering, Bandra(W), Institute of Technology, Wadala€, Mumbai-400037, India 2015.

N. Bacalbasa, A. Gireada, I. Balescu. “Tumor markers in pancreatic cancer”, Scholarly Journal Vol. 7, Iss. 2: 75-78 Jun 2015.

S. Sasikala, M. Bharathi, R. Sowmiya “Lung cancer detection and classification Using Deep CNN”, IJITEE, 8(25)-262 December, 2018.

A. Mazin, A. Mohd, I. Raed, K. Mohamad, A. Dheyaa “Automatic segmentation and automatic seed point selection of nasopharyngeal carcinoma from microscopy images using region growing based approach”, JCSV vol 20, Pg 61-69 May 2017.

T. Khoa, K. Olga, B. Andrew, W. Elizabeth, P. John et al. “Deep learning in cancer diagnosis, prognosis, and treatment selection.” Genome Medicine 13(1) :152. September, 2021.



How to Cite

Shuaib, I., Osaghae, E. ., & Frederick, B. . (2022). The Cancer Detection using Deep Learning: A Survey : Cancer Detection using Deep Learning: A Survey . Journal of Applied Artificial Intelligence, 3(1), 75–82.