چکیده (انگلیسی):

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%.