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Automated Cancer Detection using Machine Learning and Image Processing
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 184 - Number 30 |
| Year of Publication: 2022 |
| Authors: H.M.U.S.S. Samarakoon, P.D.S. Fernando, B.A.N. Mendis, R.P.P. Kanchana, G.W.D.A. Gunarathne, L.O. Ruggahakotuwa |
10.5120/ijca2022922367
|
H.M.U.S.S. Samarakoon, P.D.S. Fernando, B.A.N. Mendis, R.P.P. Kanchana, G.W.D.A. Gunarathne, L.O. Ruggahakotuwa . Automated Cancer Detection using Machine Learning and Image Processing. International Journal of Computer Applications. 184, 30 ( Oct 2022), 27-32. DOI=10.5120/ijca2022922367
Abstract
Cancer is becoming more prevalent around the globe. Even in Sri Lanka, the total cancer rate has doubled in the last 20 years, with a corresponding increase in cancer-related deaths. Cancer is the second leading cause of hospital death. Therefore, a solution to the problem should be an arrangement to reduce time waste, a correct method of directing the patient to detect symptoms, with highly accurate cancer detection, and a better monitoring system. The proposed system is an arrangement that permits and guides a patient to recognize symptoms on their own, directing them to an appropriate healthcare specialist, accurately detecting cancer in its initial stages and monitoring the patient throughout treatment. While cancer detection systems are analyzed, the existing research studies only use one machine learning methodology at a time to diagnose cancer. In the proposed work, Convolutional Neural Network (CNN), Random Forest and XGB Classifier are applied in detecting the presence of breast cancer, brain tumor, skin cancer and lung cancer which outputs results faster with a higher accuracy. This proposed system will be published as a modern cloud-based application which provides better user-experience and ease-of-use.
References
- D. Bazazeh and R. Shubair, "Comparative study of machine learning algorithms for breast cancer detection and diagnosis," 2016.
- A. R. V. B. Soni and S. R. K, "Breast cancer detection by leveraging Machine Learning," vol. 6, no. 4, pp. 320-324, 2020.
- S. Shubham, A. Archit and C. Tanupriya, "Breast Cancer Detection Using Machine Learning Algorithms," in 2018 International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS), 2018, 2018.
- A.-H. M. R. A. A. and A. Mohannad, "Breast Cancer Detection Using K-Nearest Neighbor Machine Learning Algorithm," in 2016 9th International Conference on Developments in eSystems Engineering (DeSE), 2016, 2016.
- S. Kharya and . S. Soni, "Weighted Naive Bayes Classifier: A Predictive Model for Breast Cancer Detection," International Journal of Computer Applications, vol. 133, p. 0975 – 8887, 2016.
- K.D.Miller, Q.T.Ostrom, C.Kruchko, N. Patil, T.Tihan, G.Cioffi, H.E.Fuchs, K.A.Waite, A.Jemal, R.L.Siegel, S.Barnholtz, "Brain and other central nervous system tumor statistics," a cancer journal for clinicians, 2021.
- V.S. Lotlikar ,N. Satpute, A. Gupta., "Brain Tumor Detection Using Machine Learning and Deep Learning: A Review. Curr Med Imaging," in Curr Med Imaging, 2022.
- P. Chaturvedi,A. Jhamb, M. Vanani, V. Nemade, "Prediction and Classification of Lung Cancer Using Machine Learning Techniques," in IOP Publishing Ltd, Jaipur, India, 2021.
- S. P. a. H. Z. Rahman, "A New Method for Lung Nodule Detection Using Deep Neural Networks for CT Images," in Int. Conf. on Electrical, Computer and Communication Engineering (ECCE) pp. 1-6, 2019.
- N. M. a. A. L. C. Pehrson, "Automatic pulmonary nodule detection applying deep learning or machine learning algorithms to the LIDC-IDRI database: a systematic review Diagnostics," 2019.
- M. K. Monika, N. A. Vignesh, C. U. Kumari, "Skin cancer detection and classification using machine learning, Materials Today: Proceedings".
- J. Ferlay, M. Colombet, I. Soerjomataram, D.M. Parkin, M. Pineros, A. Znaor, F. Bray, "statistics for the year 2020: An overview.," in Int. J. Cancer, 2021.
- Z. Apalla, D. Nashan, R.B. Weller, X. Castellsaque, "Skin cancer: Epidemiology, disease burden, pathophysiology, diagnosis and therapeutic approaches," in Dermatol. Ther. , 2017, 7.
- L.E. Davis, S.C. Shalin, A.J. Tackett, "Current state of melanoma diagnosis and treatment.," in Cancer Biol. Ther, 2019.
- J. Malvehy, G. Pellacani, "Dermoscopy, confocal microscopy and other non-invasive tools for the diagnosis of non-melanoma skin cancers and other skin conditions.," in Acta Derm. Venereol, 2017.
- S. Sengupta, N. Mittal, M. Modi, "Improved skin lesions detection using color space and artificial intelligence techniques.," 2020.
- H.A. Haenssle, C. Fink, R. Schneiderbauer, F. Toberer, T. Buhl, A. Blum, A. Kalloo, A.B.H. Hassen, L. Thomas, A. Enk, "Man against machine: Diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists," 2018.
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