Feature Importance for Human Epithelial (HEp-2) Cell Image Classification

Abstract

Indirect Immuno-Fluorescence (IIF) microscopy imaging of human epithelial (HEp-2) cells is a popular method for diagnosing autoimmune diseases. Considering large data volumes, computer-aided diagnosis (CAD) systems, based on image-based classification, can help in terms of time, effort, and reliability of diagnosis. Such approaches are based on extracting some representative features from the images. This work explores the selection of the most distinctive features for HEp-2 cell images using various feature selection (FS) methods. Considering that there is no single universally optimal feature selection technique, we also propose hybridization of one class of FS methods (filter methods). Furthermore, the notion of variable importance for ranking features, provided by another type of approaches (embedded methods such as Random forest, Random uniform forest) is exploited to select a good subset of features from a large set, such that addition of new features does not increase classification accuracy. In this work, we have also, with great consideration, designed class-specific features to capture morphological visual traits of the cell patterns. We perform various experiments and discussions to demonstrate the effectiveness of FS methods along with proposed and a standard feature set. We achieve state-of-the-art performance even with small number of features, obtained after the feature selection.

Image analysis for automatic measurement of crustose lichens

“…Image classification using classification algorithms and learning-based algorithms have been implemented for a wide range of applications and scales. From the landscape scale, such as classification of land use and plant ecological units (co-occurring plant species) based on multi-spectral satellite imagery [28,29,30], to the hand specimen scale, such as medical image detection and diagnosis of radiology data [31], and reaching the microscopical scale, such as cell image classification for medical diagnose [32].…”

Research on image classification model based on deep convolution neural network

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