Early Stage Detection of Precancer using Variational Mode Decomposition and Artificial Neural Network

In this contribution, combined variational mode decomposition (VMD) aided non-linear feature descriptors & artificial neural network (ANN) for identification of different healthy and precancerous cervical tissues. Owing to the inherent problems of background laser system noise interferences in elastic scattering spectroscopic data, VMD method being noise robust is of paramount interest. VMD is used to decompose the normalized spectral data into 2 modes for analysis and attributes extraction. For each of these VMD separated modes, non-linear entropy and multifractal features, namely Shannon entropy (SE), Renyi entropy (RE), Tsallis entropy (TE) and Singularity spectrum width (SSW) are extracted to form the feature set. The extracted features are subjected to analysis of variance (ANOVA) test for subsequent feature ranking & selection of the statistically most significant features. The designated features are trained with ANN to classify the backscattered tissue spectra into healthy and cancerous ones.