Capability of new features from FTIR spectral of cervical cells for cervical precancerous diagnostic system using MLP networks

The applicability and reliability of Infrared (IR) spectroscopy to distinguish normal and abnormal cells has opened this research to obtain new features from IR spectral of cervical cells to be fed into multilayered perceptrons (MLP) networks. In order for neural networks to be used as cervical precancerous diagnostic system, the features of cervical cell were used as inputs for neural networks and the classification of cervical cell types were used as output target. For cervical cell classification, this study proposes new features of cervical cell spectrum that are suitable and can be used as inputs for neural networks. The new cervical cell features were extracted from ThinPrep^® spectrum and their applicability were tested by using seven types of MLP training algorithm. The MLP network trained using Levenberg-Marquardt Backpropogation (trainlm) algorithm presented the highest accuracy with percentage of 97.3%. The result shows that the proposed features i.e. area under spectrum at 1800-1500 cm^-1, area under spectrum at 1200-1000 cm^-1, area under spectrum at 1800-950 cm^-1, height of slope at 1650-1550 cm^-1, corrected area of protein band at 1590-1490 cm^-1, corrected area of glycogen band at 1134-985 cm^-1, corrected peak height protein (H1545) and corrected peak height glycogen (H1045) are applicable to be fed as input to neural network for cervical spectra classification in cervical precancerous diagnostic system.