AIMIC: Deep Learning for Microscopic Image Classification

Background and Objective: Deep learning techniques are powerful tools for image analysis. However, the lack of programming experience makes it difficult for novice users to apply this technology. This project aims to lower the barrier for clinical users to implement deep learning methods in microscopic image classification.Methods: In this study, an out-of-the-box software, AIMIC (artificial intelligence-based microscopy image classifier), was developed for users to apply deep learning technology in a code-free manner. The plat-form was equipped with state-of-the-art deep learning techniques and data preprocessing approaches. Furthermore, we evaluated the built-in networks on four benchmark microscopy image datasets to assist entry-level practitioners in selecting a suitable algorithm.Results: The entire deep learning pipeline, from training a new network to inferring unseen samples using the trained model, could be implemented on the proposed platform without the need for pro-gramming. In the evaluation experiments, the ResNeXt-50-32 x4d outperformed other competitor algo-rithms in terms of average accuracy (96.83%) and average F1-score (96.82%). In addition, the MobileNet-V2 achieved a good balance between the performance (accuracy of 95.72%) and computational cost (in-ference time of 0.109s for identifying one sample).Conclusions: The proposed AI platform allows people without programming experience to use artificial intelligence methods in microscopy image analysis. Besides, the ResNeXt-50-32 x4d is a preferable solu-tion for microscopic image classification, and MobileNet-V2 is most likely to be an alternative selection for the scenario when computing resources are limited.(c) 2022 Elsevier B.V. All rights reserved.

Automated summary

This study developed a software named AIMIC that allows users to apply deep learning technology for microscopic image classification without coding. In evaluation experiments, ResNeXt-50-32 x4d outperformed other algorithms, while MobileNet-V2 achieved a good balance between performance and computational cost.