Efficient Training of Colorectal Cancer Diagnosis Model through Unsupervised Learning Composite Network
Main Article Content
Abstract
The use of machine learning algorithms for precise and effective colorectal cancer diagnosis has gained popularity as a result of advancements in medical imaging. Through the use of an Unsupervised Learning Composite Network (ULCN) model this study suggests a novel method for training a diagnosis model. Conventional training techniques for these models frequently rely on sizable labelled datasets, which can be labour and resource-intensive to create. The ULCN, on the other hand, incorporates unsupervised learning into the training process, decreasing the need on labelled input. In order to address this issue, our study offers a revolutionary method known as the DL-Kmeans (Deep Learning with Unsupervised K-Means Clustering), which combines the effectiveness of the unsupervised K-means clustering algorithm with the power of deep learning. When compared to manual screening and annotation techniques, the DL-Kmeans ability to more quickly refine medical images serves as evidence of its effectiveness. The use of DL- Kmeans processed photos resulted in a two-fold acceleration in the training process for deep learning models used to diagnose colorectal cancer, which is very notable. Notably, this speeding up did not degrade performance quality; the DL- Kmeans enhanced method showed superior results in terms of training loss and accuracy attained. DL- Kmeans importance goes beyond only improving images. It is a useful tool for handling the growing volume of medical photos, which will help with the later creation of artificial intelligence models.
Article Details
This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.
References
Kumar, N., Sharma, M., Singh, V.P., Madan, C., Mehandia, S., “An empirical study of handcrafted and dense feature extraction techniques for lung and colon cancer classification from histopathological images”, Biomed. Signal Process. Control 2022, 75, 103596.
Masud, M.; Sikder, N.; Nahid, A.-A.; Bairagi, A.K.; AlZain, M.A, “A Machine Learning Approach to Diagnosing Lung and Colon Cancer Using a Deep Learning-Based Classification Framework”, Sensors 2021, 21, 748.
Talukder, M.A.; Islam, M.M.; Uddin, M.A.; Akhter, A.; Hasan, K.F.; Moni, M.A. “Machine learning-based lung and colon cancer detection using deep feature extraction and ensemble learning”, Expert Syst. Appl. 2022, 205, 117695.
Mangal, S.; Chaurasia, A.; Khajanchi, A. “Convolution neural networks for diagnosing colon and lung cancer histopathological images”, arXiv 2020, arXiv:2009.03878.
Hatuwal, B.K.; Thapa, H.C., “Lung cancer detection using convolutional neural network on histopathological images”, Int. J. Comput. Trends Technol 2020, 68, 21–24.
Ali, M.; Ali, R., “Multi-input dual-stream capsule network for improved lung and colon cancer classification”, Diagnostics 2021, 11, 1485.
Hasan, I.; Ali, S.; Rahman, H.; Islam, K., “Automated Detection and Characterization of Colon Cancer with Deep Convolutional Neural Networks”, J. Healthc. Eng. 2022, 2022, 5269913.
Bukhari, S.U.K.; Syed, A.; Bokhari, S.K.A.; Hussain, S.S.; Armaghan, S.U.; Shah, S.S.H., “The histological diagnosis of colonic adenocarcinoma by applying partial self-supervised learning”, MedRxiv 2020.
Iizuka, O. et al. Deep learning models for histopathological classification of gastric and colonic epithelial tumours. Sci. Rep. 10(1), 1–11. https://doi.org/10.1038/s41598-020-58467-9 (2020).
Mukhopadhyay, S. et al. Whole slide imaging versus microscopy for primary diagnosis in surgical pathology. Am. J. Surg. Pathol. 42, 39–52 (2018).
Kainz, P., Pfeiffer, M. &Urschler, M. Segmentation and classification of colon glands with deep convolutional neural networks and total variation regularization. PeerJ 2017(10), 1–28.
Malik, J. et al. Colorectal cancer diagnosis from histology images: A comparative study. arXiv. Published online March 26, 2019:1–12. Accessed 28 February 2021. arXiv:1903.11210.
Lui, T. K. L., Guo, C. G. & Leung, W. K. Accuracy of artificial intelligence on histology prediction and detection of colorectal polyps: A systematic review and meta-analysis. Gastrointest. Endosc. 92(1), 11-22. e6.
Dembrower, K. et al. Effect of artificial intelligence-based triaging of breast cancer screening mammograms on cancer detection and radiologist workload: A retrospective simulation study. Lancet Digit. Health 2(9), e468–e474.
Ajani, S.N., Mulla, R.A., Limkar, S. et al. DLMBHCO: design of an augmented bioinspired deep learning-based multidomain body parameter analysis via heterogeneous correlative body organ analysis. Soft Comput (2023). https://doi.org/10.1007/s00500-023-08613-y
Khetani, V., Gandhi, Y., Bhattacharya, S., Ajani, S. N., & Limkar, S. (2023). Cross-Domain Analysis of ML and DL: Evaluating their Impact in Diverse Domains. International Journal of Intelligent Systems and Applications in Engineering, 11(7s), 253–262. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2951
Barisoni, L., Lafata, K. J., Hewitt, S. M., Madabhushi, A. &Balis, U. G. J. Digital pathology and computational image analysis in nephropathology. Nat. Rev. Nephrol. 16(11), 669–685.
Campanella, G. et al. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat. Med. 25(8), 1301–1309.
Bray, F. et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 68(6), 394–424.
Safiri, S. et al. The global, regional, and national burden of colorectal cancer and its attributable risk factors in 195 countries and territories, 1990–2017: A systematic analysis for the Global Burden of Disease Study 2017. Lancet Gastroenterol. Hepatol. 4(12), 913–933.
S. Ajani and M. Wanjari, "An Efficient Approach for Clustering Uncertain Data Mining Based on Hash Indexing and Voronoi Clustering," 2013 5th International Conference and Computational Intelligence and Communication Networks, 2013, pp. 486-490, doi: 10.1109/CICN.2013.106.
Morarasu, S., Haroon, M., Morarasu, B. C., Lal, K. &Eguare, E. Colon biopsies: Benefit or burden? J. Med. Life 12(2), 156–159. https://doi.org/10.25122/jml-2019-0009 (2019).
Rakha, E. A. et al. Current and future applications of artificial intelligence in pathology: A clinical perspective. J. Clin. Pathol. 74(figure 1), 1–6. https://doi.org/10.1136/jclinpath-2020-206908 (2020).
Cui, M. & Zhang, D. Y. Artificial intelligence and computational pathology. Lab. Investig. https://doi.org/10.1038/s41374-020-00514-0 (2021)
He, K.; Zhang, X.; Ren, S.; Sun, J. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 27–30 June 2016; pp. 770–778.
Zhang, X.; Zhou, X.; Lin, M.; Sun, J. Shufflenet: An extremely efficient convolutional neural network for mobile devices. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 18–22 June 2018; pp. 6848–6856.
Simonyan, K.; Zisserman, A. Very deep convolutional networks for large-scale image recognition. arXiv 2014, arXiv:1409.1556.
Howard, A.G.; Zhu, M.; Chen, B.; Kalenichenko, D.; Wang, W.; Weyand, T.; Andreetto, M.; Adam, H. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv 2017, arXiv:1704.04861.
Szegedy, C.; Liu, W.; Jia, Y.; Sermanet, P.; Reed, S.; Anguelov, D.; Erhan, D.; Vanhoucke, V.; Rabinovich, A. Going deeper with convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA, 7–12 June 2015; pp. 1–9.
Attallah, O.; Ragab, D.A. Auto-MyIn: Automatic diagnosis of myocardial infarction via multiple GLCMs, CNNs, and SVMs. Biomed. Signal Process. Control 2023, 80, 104273.
Anowar, F.; Sadaoui, S.; Selim, B. Conceptual and empirical comparison of dimensionality reduction algorithms (PCA, KPCA, LDA, MDS, SVD, LLE, ISOMAP, LE, ICA, t-SNE). Comput. Sci. Rev. 2021, 40, 100378.
Naz, J.; Sharif, M.; Raza, M.; Shah, J.H.; Yasmin, M.; Kadry, S.; Vimal, S. Recognizing Gastrointestinal Malignancies on WCE and CCE Images by an Ensemble of Deep and Handcrafted Features with Entropy and PCA Based Features Optimization. Neural Process. Lett. 2021.
Garg, I.; Panda, P.; Roy, K. A Low Effort Approach to Structured CNN Design Using PCA. IEEE Access 2020, 8, 1347–1360.
Talukder, M.A.; Islam, M.M.; Uddin, M.A.; Akhter, A.; Hasan, K.F.; Moni, M.A. Machine learning-based lung and colon cancer detection using deep feature extraction and ensemble learning. Expert Syst. Appl. 2022, 205, 117695.
Mangal, S.; Chaurasia, A.; Khajanchi, A. Convolution neural networks for diagnosing colon and lung cancer histopathological images. arXiv 2020, arXiv:2009.03878.
Hatuwal, B.K.; Thapa, H.C. Lung cancer detection using convolutional neural network on histopathological images. Int. J. Comput. Trends Technol 2020, 68, 21–24.
Ali, M.; Ali, R. Multi-input dual-stream capsule network for improved lung and colon cancer classification. Diagnostics 2021, 11, 1485.
Hasan, I.; Ali, S.; Rahman, H.; Islam, K. Automated Detection and Characterization of Colon Cancer with Deep Convolutional Neural Networks. J. Healthc. Eng. 2022, 2022, 5269913.
Mane, D., Ashtagi, R., Kumbharkar, P., Kadam, S., Salunkhe, D., Upadhye, G. (2022). An improved transfer learning approach for classification of types of cancer. Traitement du Signal, Vol. 39, No. 6, pp. 2095-2101. https://doi.org/10.18280/ts.390622
Gaikwad, Yogesh, and Jaishree Waghmare. Histogram and Feature Encoding Based Fake Colorized Image Detection Using Machine Learning. No. 6025. EasyChair, 2021.
Thatikonda, R., Vaddadi, S.A., Arnepalli, P.R.R. et al. Securing biomedical databases based on fuzzy method through blockchain technology. Soft Comput (2023). https://doi.org/10.1007/s00500-023-08355-x