Classification of Bacterial Images using Transfer Learning, Optimized Training and Resnet-50

Abstract

Bacterial image analysis using traditional laboratory methods encounters bacterial recognition errors and requires extra experience and long processing time. Therefore, the automated classification technique of bacterial images is more useful than traditional visual observations for biologists because of their accurate classification, low cost, and fast diagnosis. In this study, a method to classify bacteria images by implementing the CNN deep learning method using Transfer Learning is proposed. This trained ResNet-50 is implemented as the CNN architecture. In the training of the classification layer, SGDM optimizer is used. The classification performance for is evaluated in using confusion matrix and four performance metrics: Accuracy, Precision, Recall and Fmeasure. The Confusion Matrix and all the performance metrics show is successful in classifying bacterial images.