A fuzzy logic based approach for prediction of basal cell carcinoma and squamous cell carcinoma among the data of skin cancer

Abstract

INTRODUCTION: Both basal cell carcinoma (BCC) and squamous cell carcinoma (SCC) is a type of skin malignancy which are deadly in nature. Although both can cause a serious setback for the human body, SCC is most dangerous as per human life is concerned. OBJECTIVES: It is necessary to spot out the cases of SCC and BCC among various data of skin cancer. In this research, the same is spotted out with the help of the fuzzy logic system. METHODS: At first, the membership function is constructed from the input data provided. Then based on the generated membership function, a set of fuzzy if-then rules are created. The outliers from the source data are also removed before the generation of the fuzzy membership function. Based on the fuzzy if-then rules, the decision, whether the case is of basal cell or squamous cell carcinoma is taken. RESULTS: The output prediction by the system is compared with the actual pathological report of the patients. The comparison provides an accuracy of 94.94 percent. CONCLUSION: This paper introduces a new technique to predict the existence of multiple cancers (either basal cell or squamous cell) from the mixed cancer dataset . The outcomes of the examination on the source dataset show that the proposed system achieved extraordinary desire accuracy for the portrayal of squamous cell carcinoma and basal cell carcinoma.

An evolutionary algorithm based feature selection and fuzzy rule reduction technique for the prediction of skin cancer

“…Jha et al 5 utilized a fuzzy framework for the expectation of BCC and SCC. The creators utilized the strategy of information filtration that is anomaly expulsion from the informational collection and the development of new participation capacities for the exceptions eliminated information to anticipate the presence of BCC and SCC.…”

“…As shown by the existing work done in the field of SC prediction, the vast majority of the discussed methods are commonly centered around the separation between malign or benign skin malignant growth: there are not many endeavors 2,4,5,7 to depict the stages and types of SC. Other work principally centered around machine learning (ML) methods for prediction mechanisms.…”