ADAPTATIVE NEURO-FUZZY INFERENCE SYSTEM (ANFIS) APPLIED ON RAMAN SPECTROSCOPY SIGNALS TO DECIPHER AND CLASSIFY HEALTHY AND DAMAGED BREAST CANCER TISSUE

Abstract

Abstract: One of the most notable causes of death in the world is cancer. We know that some of these variants can be eliminated with some method such as surgery or chemotherapy. In this paper we present an optimized method which is responsible for classifying breast tissue into healthy and damaged. After getting the spectra one of the main challenges in this process is the elimination of spectral noise composed of (a) fluorescence background and (b) high frequency noise; we used Adaptative Neuro-Fuzzy Inference System (ANFIS) in combination with moving averages filter to eliminate these disturbances. When employing multicore technology to the set of biological spectra (data parallelism), we can clearly observe the significant reduction in processing time with a gain of approximately 59.67% compared to sequential process. We highlight the advantages of applying a supervised learning algorithm like ANFIS on the principal components to perform the classification of healthy and damaged tissue and the results are compared with the well-known linear regression methods and vector support machines using testing table and k fold cross validation recording Mean Square Error values of 0.00458 and 0.002254 respectively. Based on the results obtained with this method, we consider that it would be an important clinical tool for specialists for a rapid and efficient automatic detection of breast cancer and consider the possibility of being applicable to other kinds of cancer, e.g., lung, prostate, stomach. Keywords: ANFIS, Breast Cancer, Raman Spectroscopy, Automatic Detection. We highlight the advantages of applying a supervised learning algorithm like ANFIS applied on the principal components from the point cloud to perform the classification of healthy and damaged tissue and the results are compared with the well-known linear regression methods and vector support machines. We highlight the advantages of using testing table method applied on ANFIS obtaining a considerable decrease in the Mean Square Error factor with a value of 0.00458, when we used cross validation The MSE decrease to 0.002254. Based on the results obtained with this method, we consider that it would be an important clinical tool for specialists for a rapid and efficient automatic detection of breast cancer and consider the possibility of being applicable to other kinds of cancer, e.g., lung, prostate, stomach. Keywords: ANFIS, Breast Cancer, Raman Spectroscopy, Automatic Detection.