Penggunaan Logika Fuzzy dalam Deteksi Penyakit Kanker

Authors

  • Lutviana Universitas Harapan Bangsa
  • Aimar Yudhistira Universitas Harapan Bangsa
  • Anggit Wirasto Universitas Harapan Bangsa

Keywords:

Fuzzy logic, cancer detection, cancer treatment, medical image analysis, artificial intelligence

Abstract

Cancer detection and treatment is a major challenge in global health. Fuzzy logic has proven to be effective in overcoming uncertainty and ambiguity in medical data to improve the accuracy of cancer diagnosis. This article presents a review of the application of fuzzy logic in various cancer detection and treatment studies, including its use in the analysis of medical images such as CT scans and MRIs. Studies show that fuzzy logic not only improves cancer detection accuracy but also reduces the cost and time in the diagnosis process. The results suggest that a hybrid approach combining fuzzy logic with technologies such as artificial neural networks can significantly improve the efficiency and accuracy of cancer detection

References

Alexandre-Silva, V., & Cominetti, M. R. (2024). Unraveling the dual role of ADAM10: Bridging the gap between cancer and Alzheimer’s disease. Mechanisms of Ageing and Development, 219, 111928. https://doi.org/10.1016/j.mad.2024.111928

Amini, F., Amjadifard, R., & Mansouri, A. (2024). Fuzzy information granulation towards benign and malignant lung nodules classification. Computer Methods and Programs in Biomedicine Update, 5, 100153. https://doi.org/10.1016/j.cmpbup.2024.100153

An Overview of Cancer. (2022). In Fundamentals of Cancer Detection, Treatment, and Prevention (pp. 1–20). Wiley. https://doi.org/10.1002/9783527838561.ch1

Ayenigbara, I. O. (2023). Risk-Reducing Measures for Cancer Prevention. Korean Journal of Family Medicine, 44(2), 76–86. https://doi.org/10.4082/kjfm.22.0167

Babichev, S., Yasinska-Damri, L., & Liakh, I. (2023). A Hybrid Model of Cancer Diseases Diagnosis Based on Gene Expression Data with Joint Use of Data Mining Methods and Machine Learning Techniques. Applied Sciences, 13(10), 6022. https://doi.org/10.3390/app13106022

Bandopadhyay, S., & Phadke, A. C. (2022). CNN and Fuzzy logic based hybrid approach for lung cancer detection and report generation. 2022 International Interdisciplinary Humanitarian Conference for Sustainability (IIHC), 818–822. https://doi.org/10.1109/IIHC55949.2022.10060729

Bhattacharya, A., & Pal, M. (2024). Prediction on nature of cancer by fuzzy graphoidal covering number using artificial neural network. Artificial Intelligence in Medicine, 148, 102783.

https://doi.org/10.1016/j.artmed.2024.102783

Boadh, R., Aarya, D. D., Dahiya, M., Rathee, R., Rathee, S., Kumar, A., Jain, S., & Rajoria, Y. K. (2022). Study and prediction of prostate cancer using fuzzy inference system. Materials Today: Proceedings, 56, 157–164. https://doi.org/10.1016/j.matpr.2022.01.040

Boadh, R., Grover, R., Dahiya, M., Kumar, A., Rathee, R., Rajoria, Y. K., Rawat, M., & Rani, S. (2022). Study of fuzzy expert system for the diagnosis of various types of cancer. Materials Today: Proceedings, 56, 298–307. https://doi.org/10.1016/j.matpr.2022.01.161

Borah, R., Barman, B., & Choudhury, H. (2022). Envisioning the financial and psychological hardships of cancer.

Indian Journal of Public Health, 66(2), 190. https://doi.org/10.4103/ijph.ijph_1844_21

Cooper, C., Booth, A., Varley-Campbell, J., Britten, N., & Garside, R. (2018). Defining the process to literature searching in systematic reviews: a literature review of guidance and supporting studies. BMC Medical Research Methodology, 18(1), 85. https://doi.org/10.1186/s12874-018-0545-3

Dhawan, S., Bhuyan, H. K., Pani, S. K., Ravi, V., Gupta, R., Rana, A., & Al Mazroa, A. (2024). Secure and resilient improved image steganography using hybrid fuzzy neural network with fuzzy logic. Journal of Safety Science and Resilience, 5(1), 91–101. https://doi.org/10.1016/j.jnlssr.2023.12.003

Dubey, P., Kumar, S., Behera, S. K., & Mishra, S. K. (2023). A Takagi–Sugeno fuzzy controller for minimizing cancer cells with application to androgen deprivation therapy. Healthcare Analytics, 4, 100277. https://doi.org/10.1016/j.health.2023.100277

Faisal, R. H., Debnath, S., Islam, M. M. U., Sifath, S., Kakon, S. A., Alam, M. S., & Siddique, N. (2023). A modular fuzzy expert system for chemotherapy drug dose scheduling. Healthcare Analytics, 3, 100139. https://doi.org/10.1016/j.health.2023.100139

Fordellone, M., De Benedictis, I., Bruzzese, D., & Chiodini, P. (2023). A Maximum-Entropy Fuzzy Clustering Approach for Cancer Detection When Data Are Uncertain. Applied Sciences, 13(4), 2191. https://doi.org/10.3390/app13042191

Joshi, P., & Dhar, R. (2022). EpICC: A Bayesian neural network model with uncertainty correction for a more accurate classification of cancer. Scientific Reports, 12(1), 14628. https://doi.org/10.1038/s41598-022- 18874-6

Junio Guimarães, A., Vitor de Campos Souza, P., Jonathan Silva Araújo, V., Silva Rezende, T., & Souza Araújo, V. (2019). Pruning Fuzzy Neural Network Applied to the Construction of Expert Systems to Aid in the Diagnosis of the Treatment of Cryotherapy and Immunotherapy. Big Data and Cognitive Computing, 3(2), 22. https://doi.org/10.3390/bdcc3020022

Khalsan, M., Mu, M., Al-Shamery, E. S., Machado, L., Ajit, S., & Agyeman, M. O. (2023). Fuzzy Gene Selection and Cancer Classification Based on Deep Learning Model. http://arxiv.org/abs/2305.04883

Korenevskiy, N. A., Belozerov, V. A., Al-Kasasbeh, R. T., Al-Smadi, M. M., Aikeyeva, A. A., Al-Jundi, M., Rodionova, S. N., Filist, S.,

Alshamasin, M. S., Al-Habahbeh, O. M., & Maksim, I. (2023). Differential Diagnosis of Pancreatic Cancer and Chronic Pancreatitis

According to Endoscopic Ultrasonography Based on the Analysis of the Nature of the Contours of Focal Formations Based on Fuzzy Mathematical Models. Critical Reviews in Biomedical Engineering, 51(3), 59–76. https://doi.org/10.1615/CritRevBiomedEng.2023048046

Korenevskiy, N. A., Belozerov, V. A., Al-kasasbeh, R. T., Al-Smadi, M. M., Krutskikh, V., Shalimova, E., Al-Jundi, M., Rodionova, S. N., Filist, S., Shaqadan, A., Maksim, I., & Al-Habahbeh, O. M. (2024). Using Fuzzy Mathematical Model in the Differential Diagnosis of Pancreatic Lesions Using Ultrasonography and Echographic Texture Analysis. Critical Reviews in Biomedical Engineering, 52(1), 1–20. https://doi.org/10.1615/CritRevBiomedEng.2023049762

Kraus, S., Breier, M., & Dasí-Rodríguez, S. (2020). The art of crafting a systematic literature review in entrepreneurship research. International Entrepreneurship and Management Journal, 16(3), 1023–1042.https://doi.org/10.1007/s11365-020-00635-4

Nguyen Thu Hien, Nguyen Phuong Nhung, & Nguyen Tuan Linh. (2022). Adaptive neuro-fuzzy inference system classifier with interpretability for cancer diagnostic. Journal of Military Science and Technology, CSCE6, 56– 64. https://doi.org/10.54939/1859-1043.j.mst.CSCE6.2022.56-64

Orazayeva, A., Wójcik, W., Pavlov, S., Tussupov, J., Prokopovich, I., Kovalchuk, O. ., Smailova, S., & Zhunissova, U. (2022). Imaging fuzzy expert system for assessing dynamic changes in biomedical tumor images in breast cancer. In R. S. Romaniuk, A. Smolarz, & W.

Wójcik (Eds.), Photonics Applications in Astronomy, Communications, Industry, and High Energy Physics Experiments 2022 (p. 3). SPIE. https://doi.org/10.1117/12.2657923

Patira, R., & Kumar Gupta, Y. (2023). Cancer Detection: A Review Using Fuzzy Based Learning System. 2023 1st International Conference on Intelligent Computing and Research Trends (ICRT), 1–5. https://doi.org/10.1109/ICRT57042.2023.10146631

Pramanik, R., Biswas, M., Sen, S., Souza Júnior, L. A. de, Papa, J. P., & Sarkar, R. (2022). A fuzzy distance-based ensemble of deep models for cervical cancer detection. Computer Methods and Programs in Biomedicine, 219, 106776. https://doi.org/10.1016/j.cmpb.2022.106776

Rehman, M. U., Shafique, A., Ghadi, Y. Y., Boulila, W., Jan, S. U., Gadekallu, T. R., Driss, M., & Ahmad, J. (2022). A Novel Chaos-Based Privacy-Preserving Deep Learning Model for Cancer Diagnosis. IEEE Transactions on Network Science and Engineering, 9(6), 4322–4337. https://doi.org/10.1109/TNSE.2022.3199235

Salem, H., Shams, M. Y., Elzeki, O. M., Abd Elfattah, M., F. Al-Amri, J., & Elnazer, S. (2022). Fine-Tuning Fuzzy KNN Classifier Based on Uncertainty Membership for the Medical Diagnosis of Diabetes. Applied Sciences, 12(3), 950. https://doi.org/10.3390/app12030950

Sánchez, J. A., Melendi, D., García, R., Pañeda, X. G., Corcoba, V., & García, D. (2024). Distributed and collaborative system to improve traffic conditions using fuzzy logic and V2X communications. Vehicular Communications, 47, 100746. https://doi.org/10.1016/j.vehcom.2024.100746

Shah, P., & Shah, T. (2024). Adaptive Neuro Fuzzy Inference System based classifier in diagnosis of breast cancer.

Results in Control and Optimization, 14, 100358. https://doi.org/10.1016/j.rico.2023.100358

Sotirov, S., Kostadinov, T., & Hristov, S. (2023). An Intuitionistic Fuzzy Estimation Approach on a Magnetic Resonance Imaging (pp. 47–52). https://doi.org/10.1007/978-3-031-31069-0_6

Sweidan, S., Zamzami, N., & Sabbeh, S. F. (2023). Fuzzy ontology-based approach for liver fibrosis diagnosis. Journal of King Saud University - Computer and Information Sciences, 35(8), 101720. https://doi.org/10.1016/j.jksuci.2023.101720

Titaley, J. (2023). EXPERT SYSTEM FOR DIAGNOSING DISEASE USING FUZZY LOGIC. JURNAL ILMIAH SAINS, 114–

https://doi.org/10.35799/jis.v8i1.48091

Voumik, L. C., Karthik, R., Ramamoorthy, A., & Dutta, A. (2023). A Study on Mathematics Modeling using Fuzzy Logic and Artificial Neural Network for Medical Decision Making System. 2023 International Conference on Computational Intelligence and Sustainable Engineering Solutions (CISES), 492–498. https://doi.org/10.1109/CISES58720.2023.10183534

Yalcinkaya, F., & Erbas, A. (2021). Convolutional Neural Network and Fuzzy Logic-based Hybrid Melanoma Diagnosis System. Elektronika Ir Elektrotechnika, 27(2), 55–63. https://doi.org/10.5755/j02.eie.28843

Downloads

Published

2024-09-09

How to Cite

Lutviana, Aimar Yudhistira, & Anggit Wirasto. (2024). Penggunaan Logika Fuzzy dalam Deteksi Penyakit Kanker. Jurnal Kolaborasi Riset Sarjana, 1(1), 15–32. Retrieved from https://ejournal.uhb.ac.id/index.php/korisa/article/view/1623

Issue

Section

Articles