Abstract
The early detection of cancer is crucial for succes sful treatment. Medical researchers have investigat ed a number of early-diagnosis techniques. Recently, the y have discovered that some cancers affect the concentration of certain molecules in the blood, wh ich allows early diagnosis by analyzing the blood m ass spectrum. Researchers have developed several techniques for the analysis of the mass-spectrum curve analysis and used them for the detection of prostat e, ovarian, breast, bladder, pancreatic, kidney, li ver and colon cancers. In this study we propose a new techn ique that uses the spectral domain features such as wavelet transform and Fourier transform for the ana lysis of the ovarian cancer data to differentiate b etween normal and patients with malignant cancer. We used two different classifiers for the original data, th e first one is a feed forward artificial neural network cla ssifier which gave a sensitivity of 96%, specificit y of 88% and accuracy of 94%. The second used classifier is the linear discriminant analysis classifier which separated the cancer from healthy samples with sens itivity of 79%, specificity of 75% and accuracy of about 81%. After transforming the data to the spectral do main using the Fourier transform the performance wa s degraded. The experimental results showed that the performance of the wavelet transform based system was superior to other techniques as it gave a sensitivi ty of 98%, specificity of 96% and accuracy of 95%.
Highlights
The blood mass spectrum is a curve (Fig. 2), where the x-axis shows the ratio of the weight of a specificPathological changes within an organ might be molecule to its electric charge and the y-axis is the signal reflected in proteomic patterns in serum
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%
The wavelet transform increased the performance of the feed forward artificial neural network classifier
Summary
The blood mass spectrum is a curve (Fig. 2), where the x-axis shows the ratio of the weight of a specificPathological changes within an organ might be molecule to its electric charge and the y-axis is the signal reflected in proteomic patterns in serum. The mass-spectrum developed a bioinformatics tool and used it to identify analysis is a fast inexpensive procedure based on a sample proteomic patterns in serum that distinguish neoplastic of a patient’s blood and it may potentially allow cancer from non-neoplastic disease within the ovary. In this study we propose a new technique that uses spectrometry (Fig. 1) This technology has the the spectral domain features such as wavelet transform potential to improve clinical diagnostics tests for and Fourier transform for the analysis of the ovarian cancer pathologies. When feed forward artificial distinguish between cancer and control patients. These neural network classifier is compared with LDA features will be ion intensity levels at specific classifier, it gives more efficiency than LDA but it gives mass/charge values
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
