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Iqbal, I.* ; Ullah, I.* ; Peng, T. ; Wang, W.* ; Ma, N.*

An end-to-end deep convolutional neural network-based data-driven fusion framework for identification of human induced pluripotent stem cell-derived endothelial cells in photomicrographs.

Eng. Appl. Artif. Intel. 139:109573 (2025)

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Open Access Green as soon as Postprint is submitted to ZB.

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Deep learning is a very powerful analytic tool to recognize the patterns in data to make appropriate predictions. It has tremendous potential in data analyses, particularly for cell biology domain, caused by the growing scale and inherent complexity of biological data. The core purpose of this research work is to design, implement, and calibrate an efficient deep convolutional neural network (DCNN) architecture in the context of binary-class classification problem. This diversified network is developed to precisely identify human induced pluripotent stem cell-derived endothelial cells (hiPSC-derived EC) based on photomicrograph. The proposed architecture is cerebrally developed with numerous convolutional modules, multiple kernel sizes, various pooling layers, activation functions and strides, nevertheless fewer trainable parameters to strengthen the network and enhance its performance. The proposed feature fusion framework is compared with the classifier fusion approach in terms of Matthews's correlation coefficient (MCC), training time, inference time, number of layers, number of parameters, graphics processing unit (GPU) memory utilization, and floating-point operations (FLOPS). Specifically, it achieves 94.6% sensitivity, 94.5% specificity, and 94.7% precision. Induced pluripotent stem cell (iPS) dataset is also introduced in this research work that has 16278 images which are labelled by three independent and experienced human experts of cell biology domain to facilitate future research. Experimental results show that the proposed framework offers an innovative and attainable algorithm for accelerating and systematizing the classification task along with saving time and effort.

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Publication type Article: Journal article

Document type Scientific Article

Keywords Computer Vision ; Deep Convolutional Neural Networks ; Endothelial Cells ; Human Induced Pluripotent Stem Cells ; Image Processing ; Information Fusion ; Machine Learning ; Photomicrograph

ISSN (print) / ISBN 0952-1976

e-ISSN 1873-6769

Journal Engineering Applications of Artificial Intelligence

Quellenangaben Volume: 139, Article Number: 109573

Publisher Elsevier

Publishing Place The Boulevard, Langford Lane, Kidlington, Oxford Ox5 1gb, England

Reviewing status Peer reviewed

Institute(s) Helmholtz Artifical Intelligence Cooperation Unit (HAICU)

Grants Federal Ministry of Education and Research, Germany
Helmholtz Association of German Research Centers through Program-Oriented Funding, I2B Funds (High-resolution Imaging and Computational Analysis to Study the Dynamics of Stem Cell-biomaterial Interaction)