Multi Classification of Bacterial Microscopic Images Using Inception V3

Abstract

Microorganisms such as bacteria are the main cause of various infectious diseases such as cholera, botulism, gonorrhea, Lyme disease, sore throat, tuberculosis and so on. Therefore, identification and classification of bacteria is very important in the world of medicine to help experts diagnose diseases suffered by patients. However, manual identification and classification of bacteria takes a long time and a professional individual. With the help of artificial intelligence, we can effectively and efficiently classify bacteria and save a lot of time and human labor. In this study, a system was created to classify bacteria from microscopic image samples. This system uses deep learning with the transfer learning method. Inception V3 architecture was modified and retained using 108 image samples labeled with five types of bacteria, namely Acinetobacter baumanii, Escherichia coli, Neisseria gonorrhoeae, Propionibacterium acnes and Veionella. The data is then divided into training and validation using the k-fold cross validation method. Furthermore, the features that have been extracted by the model are trained with the configuration of minibatchsize 5, maxepoch 5, initiallearnrate 0.0001, and validation frequency 3. The model is then tested with data validation by conducting ten experiments and getting an average accuracy value of 94.42%.