Research on the cancer cell's recognition algorithm based on the combination of competitive FHNN and FBPNN Online publication date: Sun, 17-Jul-2016
by Hu Qi; Duan Jin; Zhai Di
International Journal of Computing Science and Mathematics (IJCSM), Vol. 7, No. 3, 2016
Abstract: In the field of traditional medicine, diagnosis on the microscopic image of cancer cells fallen into peritoneal effusion can often be recognised by the naked eye observation from personal experience, but uncertain factors such as intake angle and method of microscopic image will lead to frequent phenomenon such as misdiagnosis or cases that cannot be confirmed. A competitive fuzzy Hopfield neural network (FHNN) automatic clustering segmentation is used in this paper to segment suspected cells and nucleus from the microscopic image of peritoneal effusion exfoliated cells and to extract 15 features of cancer cells. Flexible BP neural network (FBPNN) is used in this paper as classifier that classifies and recognises the peritoneal effusion exfoliated cells. By analysing clinical cases, the proposed algorithm has been proved that it can acquire higher accuracy of cancer cells diagnosis.
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