SPECTRAL DOMAIN FEATURES FOR OVARIAN CANCER DATA ANALYSIS

Abstract

The early detection of cancer is crucial for successful treatment. Medical researchers have investigated a number of early-diagnosis techniques. Recently, they have discovered that some cancers affect the concentration of certain molecules in the blood, which allows early diagnosis by analyzing the blood mass spectrum. Researchers have developed several techniques for the analysis of the mass-spectrum curve analysis and used them for the detection of prostate, ovarian, breast, bladder, pancreatic, kidney, liver and colon cancers. In this study we propose a new technique that uses the spectral domain features such as wavelet transform and Fourier transform for the analysis of the ovarian cancer data to differentiate between normal and patients with malignant cancer. We used two different classifiers for the original data, the first one is a feed forward artificial neural network classifier which gave a sensitivity of 96%, specificity of 88% and accuracy of 94%. The second used classifier is the linear discriminant analysis classifier which separated the cancer from healthy samples with sensitivity of 79%, specificity of 75% and accuracy of about 81%. After transforming the data to the spectral domain using the Fourier transform the performance was degraded. The experimental results showed that the performance of the wavelet transform based system was superior to other techniques as it gave a sensitivity of 98%, specificity of 96% and accuracy of 95%.