Bacterial Strain Classification using Convolutional Neural Network for Automatic Bacterial Disease Diagnosis

Abstract

Diseases caused by bacterial contamination are common causes of human illness. Different bacterial strains are responsible for different types of diseases. There are more than 4,900 different strains so far have been discovered. That is why it is impractical to start the treatment of diseases caused by bacterial attacks without diagnosing the particular strain that caused the diseases. The traditional method of bacterial strain classification from the specimens is still widely used in microbiological practice for clinical application. However, it s a time-consuming process and requires well-trained, experienced microbiologists. This paper proposes a computer-aided artificial intelligent-based automatic bacterial strain classification method that is faster than traditional methods and a potentially better alternative. We designed, optimized, and experimented with a Convolutional Neural Network (CNN) to automatically classify bacterial strains from the digital images of the bacterial strains captured using an SC30 camera from an Olympus CX31 Upright Biological Microscope. The proposed network classifies the bacterial strains with 95.12% accuracy, 96.01% precision, 96.70% recall, and 4.88% error rate. This paper uses an innovative image augmentation method to overcome the limitation of the number of training images. The proposed network performs better than similar approaches regarding classification accuracy and network simplicity.