GFP Pattern Recognition in Raman Spectra by Modified VGG Networks for Localisation Tracking in Living Cells

The coupling between Raman spectroscopy and green fluorescent protein (GFP) labelling informs chemical compositions at the specific sites. This information leading to study that explain core knowledge of living organism and eventually advance our conventional technique of medical diagnosis. In order to achieve these purposes, the precise interpretation is required. A massive number of Raman/GFP spectra as well as identification of GFP contribution in each spectrum are arroaches to achieve those goals. In the paper, CNN is proposed to classify the spectra with and without GFP signal. The dataset of GFP-positive and GFP-negative spectra were created with various size and background color. The feature extraction and classification are conduced with VGG networks. To increase the performance of VGG network, the modified VGG13 and modified VGG19 were designed. These two models extend fully-connected layer from 3 (the original VGG model) to 5 layer for better classification task. Batch normalization is also added at the end of feature extraction units to reduce unpredicted shifting of parameters. The original VGG16, VGG19, and ResNet50 are used as comparison models. The results show that both of our modified VGG models significantly enhances training accuracy of the network comparing to the original VGG. The accuracy of original VGG can be increased when applied pre-trained weight, but the accuracies are yet slightly lower than modified models. Training on ResNet, deeper network, gave the comparable accuracy with our modified models

AN EFFECTIVE METHOD FOR ATTRIBUTE SUBSET SELECTION, CONSIDERING THE RESOURCE IN PATTERN RECOGNITION

An analytical method for determining informative sets of features (INP) is developed, taking into account the resource for criteria based on the use of a measure of dispersion of classified objects. The areas of existence of the solution are defined. The statements and properties for the Fischer-type information criterion are proved, using which the proposed analytical method for determining the INP guarantees optimal results in the sense of maximizing the selected functional. The appropriateness of choosing this type of informative criterion is justified. A method for transforming attributes is proposed. The universality of the method in relation to the type of features is shown. An algorithm for implementing this method is given. In addition, the paper discusses the dynamics of the growth of information volume in the world, problems related to big data, as well as problems and tasks of pre-processing data. The relevance of reducing the dimension of the feature space for performing data processing and visualization without unnecessary difficulties is proved. The disadvantages of existing methods and algorithms for selecting an informative set of features are shown