Classification of Microscopic Images of Bacteria using Deep Convolutional Neural Network |
Author(s): |
Rishab Khar , Dr. DY Patil School of Engineering Academy, Ambi, Pune; Ritesh Kumar , Dr. DY Patil School of Engineering Academy, Ambi, Pune; Piyush Tajne, Dr. DY Patil School of Engineering Academy, Ambi, Pune; Prachi Salve, Dr. DY Patil School of Engineering Academy, Ambi, Pune |
Keywords: |
Bacteria classification, DCNN, Inception DCNN model, microscopic image, transfer learning |
Abstract |
With the advancement of technology, now the task of recognizing images from digital electron microscopes is being performed by computers supported machine-learning and computer-vision technologies. Besides, the newest generation of convolutional neural networks (CNN) have achieved impressive leads to the sector of image classification recently. Thus, during this paper, we have investigated an approach to automate the method of bacteria recognition and classification with the utilization of deep convolutional neural network (DCNN). We have used the transfer learning method to retrain the Inception DCNN model with of a dataset of quite 500 microscopic images of 5 different species that are harmful to human-health. 20% images of the dataset were randomly chosen and separated, which were wont to test the classification accuracy of the network. The retrained model was ready to recognize and classify all 5 different species of bacteria, while the experimental results of prediction achieved accuracy of around 95%. |
Other Details |
Paper ID: IJSRDV8I20114 Published in: Volume : 8, Issue : 2 Publication Date: 01/05/2020 Page(s): 110-112 |
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