Deep learning based microscopic cell images classification framework using multi-level ensemble

Abstract

Background and objectivesAdvancement of the ultra-fast microscopic images acquisition and generation techniques give rise to the automated artificial intelligence (AI)-based microscopic images classification systems. The earlier cell classification systems classify the cell images of a specific type captured using a specific microscopy technique, therefore the motivation behind the present study is to develop a generic framework that can be used for the classification of cell images of multiple types captured using a variety of microscopic techniques. MethodsThe proposed framework for microscopic cell images classification is based on the transfer learning-based multi-level ensemble approach. The ensemble is made by training the same base model with different optimisation methods and different learning rates. An important contribution of the proposed framework lies in its ability to capture different granularities of features extracted from multiple scales of an input microscopic cell image. The base learners used in the proposed ensemble encapsulates the aggregation of low-level coarse features and high-level semantic features, thus, represent the different granular microscopic cell image features present at different scales of input cell images. The batch normalisation layer has been added to the base models for the fast convergence in the proposed ensemble for microscopic cell images classification. ResultsThe general applicability of the proposed framework for microscopic cell image classification has been tested with five different public datasets. The proposed method has outperformed the experimental results obtained in several other similar works. ConclusionsThe proposed framework for microscopic cell classification outperforms the other state-of-the-art classification methods in the same domain with a comparatively lesser amount of training data.