Abstract
The primary difficult in medical fields is the mining of understandable information from medical analysis data. The growing of medical data has made labour-intensive analysis, a tiresome job and sometimes not possible by medical experts. Many unknown and hypothetically valuable associations are not be recognized by the expert. The massive development of images necessitates a programmed manner to excerpt valuable information. The data mining or Knowledge Discovery Databases is main promising approach to solve this problem. Fruitful and interesting information can be mined and the discovered information can be used in the associated domain to improve the working level and to increase the feature of decision making through data mining. A significant task in knowledge discovery is to mine intelligible classification rules from the data. These rules are mainly informative for medical issues which are tremendously useful especially in the application of medical diagnosis. Automatic extraction of hidden information from images is a challenging task. The field of automated diagnostic systems performs an important part in the present technological revolution of computerized fully automated trend of living. The main aim of this research work is to extract tumor stage information. In this research, presents a method for extricating phases of cancer via Adaptive Neuro-Fuzzy Inference System (ANFIS), it has been given a more precise result than other methods. ANFIS is exhibited as a diagnostic tool to aid medical experts in the identification of tumor stages.
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Gomathi, V.V., Karthikeyan, S., Madhu Sairam, R. (2021). Knowledge Discovery from Tumor Volume Using Adaptive Neuro Fuzzy Inference System Rules. In: Mahmud, M., Kaiser, M.S., Kasabov, N., Iftekharuddin, K., Zhong, N. (eds) Applied Intelligence and Informatics. AII 2021. Communications in Computer and Information Science, vol 1435. Springer, Cham. https://doi.org/10.1007/978-3-030-82269-9_10
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