Open AccessAIDeveloper: Deep Learning Image Classification in Life Science and Beyond
Author(s) -
Kräter Martin,
Abuhattum Shada,
Soteriou Despina,
Jacobi Angela,
Krüger Thomas,
Guck Jochen,
Herbig Maik
Publication year - 2021
Publication title -
advanced science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.388
H-Index - 100
ISSN - 2198-3844
DOI - 10.1002/advs.202003743
Subject(s) - mnist database , computer science , artificial intelligence , image (mathematics) , task (project management) , variety (cybernetics) , contextual image classification , deep learning , machine learning , software , artificial neural network , pattern recognition (psychology) , open source , deep neural networks , image processing , engineering , operating system , systems engineering
Artificial intelligence (AI)‐based image analysis has increased drastically in recent years. However, all applications use individual solutions, highly specialized for a particular task. Here, an easy‐to‐use, adaptable, and open source software, called AIDeveloper (AID) to train neural nets (NN) for image classification without the need for programming is presented. AID provides a variety of NN‐architectures, allowing to apply trained models on new data, obtain performance metrics, and export final models to different formats. AID is benchmarked on large image datasets (CIFAR‐10 and Fashion‐MNIST). Furthermore, models are trained to distinguish areas of differentiated stem cells in images of cell culture. A conventional blood cell count and a blood count obtained using an NN are compared, trained on >1.2 million images, and demonstrated how AID can be used for label‐free classification of B‐ and T‐cells. All models are generated by non‐programmers on generic computers, allowing for an interdisciplinary use.