Bacterial Colony Classification Using Atrous Convolution with Transfer Learning

Abstract

The characteristic of a colony is termed as colony morphology. The traditional way to identify and classify the bacterial colony is a visual screening performed by a trained specialist like a laboratory clinician that requires adequate experience and long executing time. The advancement in the field of artificial intelligence along with computer vision provided an opportunity to automate the process of bacterial colony classification without explicit feature extraction and human intervention. Deep neural networks have  been  successfully  applied  in  many  computer  vision  tasks  in  the  recent  years. In this paper, we proposed an atrous convolution based network with transfer learning for bacterial colony classification. Atrous convolution is also referred as a dilated convolution that is mainly applied to increase the size of the receptive field. In the proposed model, atrous convolution is used to replace the convolution layer in the traditional VGG-16 convolutional neural network. The experiment is carried out on 660 bacterial colony dataset with 33 classes. The experimental results show that the proposed model is able to achieve 95.06% training accuracy, 93.38% validation accuracy and 94.85% test accuracy. The performance can be further improved with more number of bacterial colony images.