Abstract:
Bacterial Image Classification Using Deep Learning is a system which is designed to classify
bacterial images according to their species. Numerous uses for bacteria image classification
include medical diagnosis, microbiological research, quality control, environmental monitor ing, and biodefense. Traditional methods of bacteria classification involve manual observation
and analysis by experts, which can be time-consuming and prone to errors. The method of
recognizing and categorizing various species of bacteria using microscopic photographs is
known as ”bacterial image classification”. To process the image data and extract pertinent
traits that can be used to categorize various species of bacteria, using deep learning techniques
such as two CNN models, ResNet-50 and Resnet-34, as well as ANN and LSTM .
In deep learning, high-level features are extracted from input data, such as photographs, using
neural networks with several layers. Convolutional Neural Networks (CNNs), a type of deep
learning model, are used to extract pertinent characteristics from images. Convolutional filters
are used to the input picture in CNNs in order to extract spatial characteristics from images.
Two CNN models, ResNet-50 and Resnet-34, as well as ANN and LSTM is used. These models
focus on categorizing species of bacteria. Studies have demonstrated that the CNN model
ResNet-50 exhibited a classification accuracy of 100 percent and produce the best outcomes
than the Resnet-34,ANN and LSTM
