ESP Journal of Engineering & Technology Advancements |
© 2023 by ESP JETA |
Volume 3 Issue 2 |
Year of Publication : 2023 |
Authors : P. Mahalakshmi, P. Sangeetha, G. Harikrishnaveni, C. Dhivya |
:10.56472/25832646/JETA-V3I6P104 |
P. Mahalakshmi, P. Sangeetha, G. Harikrishnaveni, C. Dhivya, 2023. KNN Model for Cancer Prediction Using Stem Cells ESP Journal of Engineering & Technology Advancements 3(2): 95-100.
To develop a method for detecting and identifying the type of cancer using the k-nearest neighbor (KNN) algorithm. The KNN algorithm is a simple machine learning algorithm that classifies [1]-[3] an input sample based on the majority of its nearest neighbor in the training data. The KNN algorithm is trained on a large dataset of cancer patients and their corresponding diagnosis, and is then used to predict the type of cancer for a new patient based on their clinical and laboratory test results. The results of this study show that the KNN algorithm is able [4]- [13] to achieve high accuracy in detecting and identifying the type of cancer, making it a promising tool for supporting clinical decision-making in the early stages of cancer diagnosis.
[1] A. Jothi and V. M. A. Rajam, ‘‘A survey on automated cancer diagnosis from histopathology images,’’ Artif. Intell. Rev., vol. 48, no. 1, pp. 31– 81, Jun. 2020
[2] B. Taha, J. Dias, and N. rghi, ‘‘Classification of prostrate -cancer using pap-smear images: A convolutional neural network approach,’’ in Proc. Annu. Conf. Med. Image Understand. Anal. Berlin, Germany: Springer, 2020, pp. 261–272.
[3] B. Zhang, S. Qi, P. Monkam, C. Li, F. Yang, Y.-D. Yao, and W. Qian,`Ensemble learners of multiple deep CNNs for pulmonary nodules classi_cation using CT images,'' IEEE Access, vol. 7, pp. 110358_110371,2021
[4] C. Li, D. Xue, X. Zhou, J. Zhang, H. Zhang, Y. Yao, F. Kong, L. Zhang, and H. Sun, ``Transfer learning based classi_cation of prostrate cancer immunohistochemistry images,'' in Proc. 3rd Int. Symp. Image Comput. Digit. Med. (ISICDM), 2021, pp. 102_106.
[5] C. Li, H. Chen, L. Zhang, N. Xu, D. Xue, Z. Hu, H. Ma, and H. Sun,``Prostrate histopathology image classi_cation using multilayer hidden conditional random _elds and akly supervised learning,'' IEEE Access,vol. 7, pp. 90378_90397, 2021
[6] C. Li, H. Chen, X. Li, N. Xu, Z. Hu, D. Xue, S. Qi, H. Ma, L. Zhang,and H. Sun, "A review for prostrate histopathology image analysis using 43 machine vision approaches," Artif. Intell. Rev., pp. 1_42, Feb. 2020, doi:10.1007/s10462-020-09808-7
[7] C. Qian, Y. Yu, and Z.-H. Zhou, ``Analyzing evolutionary optimization in noisy environments,'' Evol. Comput., vol. 26, no. 1, pp. 1_41, Mar.2020.
[8] E. Goceri, B. Goksel, J. B. Elder, V. K. Puduvalli, J. J. Otero, and M.N. Gurcan, ‘‘Quantitative validation of anti-PTBP1 antibody for diagnostic neuropathology use: Image analysis approach,’’ Int. J. Numer. Methods Biomed. Eng., vol. 33, no. 11, p. e2862, Nov. 2020.
[9] F. Chollet, ‘‘Xception: Deep learning with depthwise separable convolutions,’’ 2020, arXiv:1610.02357. [Online]. Available: http://arxiv. org/abs/1610.02357
[10] F. Shoeleh and M. Asadpour, ‘‘Graph based skill acquisition and transfer learning for continuous reinforcement learning domains,’’ Pattern Recognit. Lett., vol. 87, pp. 104–116, Feb. 2021
[11] Gautam, H. K. K., N. Jith, A. K. Sao, A. Bhavsar, and A. Natarajan,"Considerations for a PAP smear image analysis system with CNN features,'' 2020, arXiv:1806.09025. [Online]. Available: http://arxiv. org/abs/1806.09025
[12] H. Komagata, T. Ichimura, Y. Matsuta, M. Ishikawa, K. Shinoda, N. Kobayashi, and A. Sasaki, ‘‘Feature analysis of cell nuclear chromatin distribution in support of prostrate cytology,’’ J. Med. Imag., vol. 4, no. 4, p. 1, Oct. 2021.
[13] J. Su, X. Xu, Y. He, and J. Song, ‘‘Automatic detection of prostrate cancer cells by a two-level cascade classification system,’’ Anal. Cellular Pathol., vol. 2016, no. 4, 2020, Art. no. 9535027.
[14] L. Nanni, S. Ghidoni, and S. Brahnam, ‘‘Handcrafted vs. Nonhandcrafted features for computer vision classification,’’ Pattern Recognit., vol. 71, pp. 158–172, Nov. 2020.
[15] L. i, Q. Gan, and T. Ji, ‘‘Prostrate cancer histology image identification method based on texture and lesion area features,’’ Comput. Assist. Surg., vol. 22, no. 1, pp. 186–199, Oct. 2021
[16] L. Zhang, L. Lu, I. Nogues, R. M. Summers, S. Liu, and J. Yao, ‘‘DeepPap: Deep convolutional networks for prostrate cell classification,’’ IEEE J. Biomed. Health Inform, vol. 21, no. 6, pp. 1633– 1643, Nov. 2021.
[17] M. A. Devi, S. Ravi, J. Vaishnavi, and S. Punitha, ‘‘Classification of prostate cancer using artificial neural networks,’’ Procedia Comput. Sci., vol. 89, pp. 465–472, 2021
[18] M. M. Ghazi, B. Yanikoglu, and E. Aptoula, ‘‘Plant identification using deep neural networks via optimization of transfer learning parameters,’’ Neurocomputing, vol. 235, pp. 228–235, Apr. 2020.
[19] M. Rahaman, C. Li, X. Wu, Y. Yao, Z. Hu, T. Jiang, X. Li, and S. Qi,``A survey for prostrate cytopathology image analysis using deep learning,''IEEE Access, vol. 8, no. 1, pp. 61687_61710, 2020.
[20] N. Crossley, C. Tipton, T. Meier, M. Sudhoff, and J. Kharofa, ``The value of hybrid interstitial tandem and ring applicators for organ at risk dose reduction in small volume prostate cancer,'' Brachytherapy, vol. 17, no. 4, p. S111, Jul. 2021.
[21] P. Mahalakshmi , T. Manigandan ,and D. Chitra, “Detecting Character in Video Frames for Traffic Vehicle” volume 4 Issue 9 – September 2017.
[22] Nitin Sonaji Magar, Zafar Ul Hasan, Anand B.Humbe, 2023. "Design and Development of Optimized Cardiovascular Disease Prediction Model using Artificial Intelligence" ESP International Journal of Advancements In Science & Technology (ESP- IJAST) Volume 1, Issue 2 : 20-27
Classifies, Training Data, Diagnosis, Detecting and Identifying Cancer, KNN.