Bacterial Image Classification Using Convolutional Neural Networks
Tumun Shaily, S Kala
Abstract
Bacteria classification is an essential task in medical field, for the diagnosis and treatment of various diseases. Typically, classification has been done by clinical specialists using conventional techniques, which do not rely on prediction approaches. Manual classification of bacteria is a time consuming and challenging task which requires huge human efforts. As technology has advanced, classification of micro-organisms have been possible with the aid of novel machine learning algorithms implemented on computers. Deep Neural Network (DNN) is one such promising technology which has been widely used for image classification. One of the variant of DNN is Convolutional Neural Network (CNN), which is an efficient technique for classification problems, has been used in this paper for bacteria classification. We have used ResNet-50 CNN model for classifying bacterial images into twenty categories, which are medically relevant. Using our approach, we could get an accuracy of 99.9% for classification. Experimental results show that our technique gives better results compared to the state-of-art approaches for bacteria classification.