Performance and Efficiency Trade-Off Analysis of Modern CNN Architectures for Automated Stem Cell Colony Quality Assessment
Year: 2026
Type: article
Abstract: Quality assessment of stem cell colonies plays a critical role in regenerative medicine and cell-based therapies. Performing this assessment manually by experts is both time-consuming and prone to inter-observer variability. In addition, the limited availability of high-quality labeled stem cell colony images increases the risk of overfitting in data-driven approaches and complicates model generalization. In this study, a systematic comparison of multiple CNN architectures is presented for the binary classification problem of stem cell colony images as good or bad, within a multi-dimensional evaluation framework incorporating performance, calibration, and efficiency. On an open-access dataset previously reported in the literature with an accuracy of approximately 89%, eight modern CNN architectures were evaluated using standard preprocessing and data augmentation strategies under a five-fold cross-validation protocol, including VGG13, VGG16, ResNet50V2, DenseNet169, InceptionV3, MobileNetV3-Small, EfficientNet-B0, and EfficientNetV2-S. The models were analyzed using conventional performance metrics such as accuracy, F1-score, ROC-AUC, and Average Precision, as well as calibration metrics including the Brier score, Expected Calibration Error, and Negative Log-Likelihood. Post-hoc calibration via temperature scaling was applied using a split-validation protocol. In addition, the models were compared in terms of computational cost including GFLOPs, VRAM consumption, number of parameters, training time, and inference latency, and data-efficiency was assessed via learning curve analysis. The results demonstrate that the EfficientNetV2-S model achieves the most balanced performance in terms of accuracy, generalization, and calibration, reaching an accuracy of 95.2%, while the EfficientNet-B0 model provides competitive performance with substantially lower computational cost. Calibration performance improves notably after post-hoc scaling, highlighting the importance of reliable confidence estimation. These findings emphasize the critical importance of balancing performance, efficiency, and reliability in model architecture selection for data-limited biomedical imaging scenarios and present a practical deep learning framework for the automated quality assessment of stem cell colonies.
Source: IEEE Access
Authors: Hatice Hilal Gurleyen, Nejat Yumusak
Institution: Sakarya University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,075
OpenAlex ID: W7160912240