Automated detection of bioimages using novel deep feature fusion algorithm and effective high-dimensional feature selection approach. (October 2021)

• Record Type: Journal Article
• Authors: Maurya, Ritesh; Pathak, Vinay Kumar; Burget, Radim; Dutta, Malay Kishore
• Is Part Of: Computers in biology and medicine. Volume 137(2021)
• Publication Date: 2021-10
• Subjects: Convolutional neural networks -- Bioimage classification -- Transfer learning -- Evolutionary algorithms -- Feature fusion -- Pre-trained CNNs; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2021.104862
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: The classification of bioimages plays an important role in several biological studies, such as subcellular localisation, phenotype identification and other types of histopathological examinations. The objective of the present study was to develop a computer-aided bioimage classification method for the classification of bioimages across nine diverse benchmark datasets. A novel algorithm was developed, which systematically fused the features extracted from nine different convolution neural network architectures. A systematic fusion of features boosts the performance of a classifier but at the cost of the high dimensionality of the fused feature set. Therefore, non-discriminatory and redundant features need to be removed from a high-dimensional fused feature set to improve the classification performance and reduce the time complexity. To achieve this aim, a method based on analysis of variance and evolutionary feature selection was developed to select an optimal set of discriminatory features from the fused feature set. The proposed method was evaluated on nine different benchmark datasets. The experimental results showed that the proposed method achieved superior performance, with a significant reduction in the dimensionality of the fused feature set for most bioimage datasets. The performance of the proposed feature selection method was better than that of some of the most recent and classical methods used for feature selection. Thus, the proposed method was desirable because of its superior performance and high compression ratio, which significantly reduced the computational complexity. Highlights: Proposed method is useful for the classification of bioimages across diverse datasets. The novel rank-based feature fusion algorithm has been proposed. Evolutionary algorithm-based feature selection algorithm has been proposed. The proposed method is computationally inexpensive and accurate.