Optimizing combination of feature extraction and classifiers in supervised classification of cells images

Developments in the medical field have opened the opportunity to conduct analyses on a personalized patient level. One of the important analyses that can be conducted is the cellular response to engineered materials, and the most appropriate non-invasive methods are imaging. These images of the cells are unstained brightfield images, as they are acquired from multiparametric microfluidic chambers in the presence of biomaterials and fluids that can change the optical path length over time as the cells’ health state is monitored. These experimental conditions lead to an image dataset with unique illumination, texture, and noise spectrum. This study explores the optimization of supervised cell classification by combining feature extraction architectures and machine learning classifiers, with a focus on applications in biomaterial risk assessment. Brightfield microscopy images of three cell types (A549, BALB 3T3, and THP1) were analyzed to evaluate the impact of Inception V3, Squeeze Net, and VGG16 architectures paired with classifiers including KNN, Decision Tree, Random Forest, AdaBoost, Neural Networks, and Naïve Bayes. Dimensionality reduction using Information Gain was applied to improve computational efficiency and accuracy. Butterworth filters with varying parameters were used to balance the enhancement of image features and noise removal, improving classification performance in certain cases. Experimental results demonstrate that the VGG16 architecture, when paired with Neural Networks, achieves higher classification accuracy as measured by different metrics. The improved accuracy when using Butterworth filters compared to the unfiltered dataset and the differences between various Butterworth filters indicate the importance of optimizing filter parameters for these types of images.