International Journal of Intelligent Information Systems
Volume 9, Issue 2, April 2020, Pages: 6-15
Received: May 14, 2020;
Accepted: Jun. 2, 2020;
Published: Jul. 4, 2020
Views 272 Downloads 518
Saif Ali, University Institute of Information Technology, PMAS Arid Agriculture University, Rawalpindi, Pakistan
Aneeqa Tanveer, University Institute of Information Technology, PMAS Arid Agriculture University, Rawalpindi, Pakistan
Azhar Hussain, University Institute of Information Technology, PMAS Arid Agriculture University, Rawalpindi, Pakistan
Saif Ur Rehman, University Institute of Information Technology, PMAS Arid Agriculture University, Rawalpindi, Pakistan
Cancer, also called malignancy, is an abnormal growth of cells. There are more than 100 types of cancer, including breast cancer, skin cancer, lung cancer, colon cancer, prostate cancer, and lymphoma. Symptoms vary depending on the type. Cancer treatment may include chemotherapy, radiation, and/or surgery. According to American Cancer Society America will be encountering 1,806,950 new cases of cancer in the year 2020 causing 606,520 deaths. Cancer is the leading cause of death in the world. Cancer can be classified into two main categories malignant and benign. Early detection of cancer is the key to the successful treatment of cancer. There are various methodologies for the detection of cancer some include manual procedures, Manual identification is time-consuming and unreliable therefore computer-aided detection came into the research. Computer-aided detection involves image processing for feature extraction and classification techniques for the recognition of cancer type and stages. In this paper, several different algorithms have been discussed such as SVM, KNN, DT, etc. for the classification of the different cancers. This paper also presents a comparative analysis of the researches done in the past.
Saif Ur Rehman,
Identification of Cancer Disease Using Image Processing Approahes, International Journal of Intelligent Information Systems.
Vol. 9, No. 2,
2020, pp. 6-15.
https://cancerstatisticscenter.cancer.org/#!/cancer-site/Leukemia [Accessed 5 Feb 2020].
https://www.hematology.org/Patients/Basics/#[Accessed 5 Feb 2020].
M. Saritha, B. Prakash, K. Sukesh and B. Shrinivas. (2016). Detection of blood cancer in microscopic images of human blood samples: A review. 596-600. 10.1109/ICEEOT.2016.7754751.
R., Adollah, M. Y., Mashor, N. F. M, Nasir, H., Rosline, H., Mahsin, H., Adilah. 2008). Blood Cell Image Segmentation: A Review. Biomed2008, Proceedings 21, 2008, pp. 141-144.
Y. Chandni and Z. Shrutika. (2018). Automatic Blood Cancer Detection Using Image Processing. International Journal of Recent Trends in Engineering & Research (IJRTER), vol. 04, no. 03, March 2018, ISSN 2455-1457.
S. Jagadeesh, E. Nagabhooshanam and S. Venkatachalam. (2013). Image processing based approach to cancer cell prediction in blood samples. International Journal of Technology and Engineering Sciences 1.1 (2013): 1-10.
Mishra, Nabin K., and Mehemmed Emre Celebi. (2016). An Overview of Melanoma Detection in Dermoscopy Images Using Image Processing and Machine Learning. ArXiv abs/1601.07843 (2016).
Sahni P., Mittal N. (2019) Breast Cancer Detection Using Image Processing Techniques. In: Kumar M., Pandey R., Kumar V. (eds) Advances in Interdisciplinary Engineering. Lecture Notes in Mechanical Engineering. Springer, Singapore.
R. Stephanie, A. Hossein, S. Kevin and H. Johan (2017). Digital image analysis in breast pathology-from image processing techniques to artificial intelligence. Transl Res. 2017 Nov 7 Published online 2017 Nov 7. doi: 10.1016/j.trsl.2017.10.010.
Abdeldaim A. M., Sahlol A. T., Elhoseny M., Hassanien A. E. (2018) Computer-Aided Acute Lymphoblastic Leukemia Diagnosis System Based on Image Analysis. In: Hassanien A., Oliva D. (eds) Advances in Soft Computing and Machine Learning in Image Processing. Studies in Computational Intelligence, vol 730. Springer, Cham.
V. Shankar, M. M. Deshpande, N. Chaitra, and S. Aditi, "Automatic detection of acute lymphoblastic leukemia using image processing," 2016 IEEE International Conference on Advances in Computer Applications (ICACA), Coimbatore, 2016, pp. 186-189. doi: 10.1109/ICACA.2016.7887948.
Sarkar, S., & Das, S. (2016). A Review of Imaging Methods for Prostate Cancer Detection: Supplementary Issue: Image and Video Acquisition and Processing for Clinical Applications. Biomedical Engineering and Computational Biology. https://doi.org/10.4137/BECB.S34255.
M a, Aswathy & Mohan, Jagannath. (2016). Detection of Breast Cancer on Digital Histopathology Images: Present Status and Future Possibilities. Informatics in Medicine Unlocked. 8. 10.1016/j.imu.2016.11.001.
Basavanhally A, Yu E, Xu J, Ganesan S, Feldman M, Tomaszewski J, Madabhushi A. Incorporating domain knowledge for tubule detection in breast histopathology using O’Callaghan neighborhoods. Proc SPIE 2011; 7963: 796310–25.
Dundar M, Badve S, Bilgin G, Raykar VC, Jain RK, Sertel O, et al. Computerized classiﬁcation of intraductal breast lesions using histopathological images. IEEE Trans Biomed Eng 2011; 58 (7): 1977–84.
A. B. Tosun and C. Gunduz-Demir, C. Graph run-length matrices for histopathological image segmentation, IEEE Transactions on Medical Imaging 30 (3), 2011, 732-566.
Veta M, van Diest PJ, Kornegoor R, Huisman A, Viergever MA, Pluim JPW. Automatic nuclei segmentation in H&E stained breast cancer histopathology images. PloS ONE 2013; 8 (7): e70221.
Jain A, Atey S, Vinayak S, Srivastava V. Cancerous cell detection using histopathological image analysis. Int J Innov Res Comput Commun Engng 2014; 2 (12): 7419–26.
Karsnas A. Image analysis methods and tools for digital histopathology applications relevant to breast cancer diagnosis. Digit Compr Summ Upps Diss Fac Sci Technol 2014: 1128.
Fernandes, Steven & Chakraborty, Baisakhi & Gurupur, Varadraj & Prabhu, Ananth. (2016). Early Skin Cancer Detection Using Computer-Aided Diagnosis Techniques. Journal of Integrated Design and Process Science. 20. 33-43. 10.3233/jid-2016-0002.
Rejintal, Ashwini & Aswini, N. (2016). Image processing based leukemia cancer cell detection. 471-474. 10.1109/RTEICT.2016.7807865.
Neelima Singh, A. Asuntha (2016). L. Lung cancer detection using medical images through image Processing, Journal of Chemical and Pharmaceutical Sciences, pp 1558-1561
Tamilmani, G. & Sivakumari, S. (2017). A survey on various data mining methods for detecting cancer cells. 242-245. 10.1109/ICSTM.2017.8089160.
Cinalli, Giuseppe & Onorini, Nicola. (2020). Biopsy of the Tumor. 10.1007/978-3-030-21299-5_4.
Candotto, Valentina & Pezzetti, F & Scarano, Antonio & Agazzi, A & Spadari, F & Palmieri, A. (2019). Liquid biopsy. Journal of biological regulators and homeostatic agents. 33.
[Online] https://www.webmd.com/cancer/what-is-a-biopsy#1 [Accessed 12 Feb 2020].
J. Alam, S. Alam, and A. Hossan, “Multi-Stage Lung Cancer Detection and Prediction Using Multi-class SVM Classifie,” Int. Conf. Comput. Commun. Chem. Mater. Electron. Eng. IC4ME2 2018, pp. 1–4, 2018.
M. Vas and A. Dessai, “Lung cancer detection system using lung CT image processing,” 2017 Int. Conf. Comput. Commun. Control Autom., pp. 1–5, 2017.
S. Makaju, P. W. C. Prasad, A. Alsadoon, A. K. Singh, and A. Elchouemi, “Lung Cancer Detection using CT Scan Images,” Procedia Comput. Sci., vol. 125, no. 2009, pp. 107–114, 2018.
P. Bhuvaneswari and A. B. Therese, “Detection of Cancer in Lung with K-NN Classification Using Genetic Algorithm,” Procedia Mater. Sci., vol. 10, no. Cnt 2014, pp. 433–440, 2015.
M. Kurkure and A. Thakare, “Classification of Stages of Lung Cancer using Genetic Candidate Group Search Approach,” IOSR J. Comput. Eng., vol. 18, no. 05, pp. 07–13, 2016.
R. Tekade and K. Rajeswari, “Lung Cancer Detection and Classification Using Deep Learning,” Proc. - 2018 4th Int. Conf. Comput. Commun. Control Autom. ICCUBEA 2018, no. 2, pp. 259–262, 2018.
A. Asuntha, A. Brindha, S. Indirani, and A. Srinivasan, “Lung cancer detection using SVM algorithm and optimization techniques,” J. Chem. Pharm. Sci., vol. 9, no. 4, pp. 3198–3203, 2016.