A Comparison of Deep Learning CNN Architecture Models for Classifying Bacteria - Suvit Poomrittigul; Wanwalee Chomkwah; Tananan Tanpatanan; Sakda Sakorntanant; Treesukon Treebupachatsakul (2022 37th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC), 2022)
Since identifying bacteria from a patient's sample for medical diagnosis purposes by the traditional approach is time-consuming and requires the pathologist's expertise to do the bacteria identification procedure. Thus, involving the deep learning model reported the capability of multi-class image classification allows us to reduce the time and increase the prediction accuracy of the bacteria identification process. This research includes 35 different bacteria species and 6 different Convolutional Neural Network (CNN) architectures. Convolutional Neural Network (CNN) architectures are LeNet-5, AlexNet, VGG-16, VGG-19, ResNet-18, and ResNet-34. The results confirmed the perceptional performance by applying Stratified K-fold cross validation with VGG-16 and observing the multi-class performance with the AUC-ROC score.
Keywords: Image Processing Techniques and Applications; Cell Image Analysis Techniques; COVID-19 diagnosis using AI