• Krishna Geetika Mannava
• Mamidipalli Raja Kalyan
• Kadiyala Sai Sri Praneeth

In the realm of microbiology and biomedical research, an accurate and rapid classification of bacterial species from microscopic images plays a pivotal role in disease diagnosis and treatment. By harnessing cutting-edge Convolutional Neural Networks (CNNs) and advanced image processing techniques, our approach not only significantly boosts the efficiency of bacterial species identification but also minimizes the need for human intervention throughout the classification process. This innovative method revolutionizes the way we identify bacterial species, making the process faster, more accurate, and less reliant on manual input. Here, we are proposing an automated classification method based on deep learning. Our approach utilizes the well-established ResNet-50 convolutional neural network (CNN) architecture, which has been pre-trained, to classify digital images of bacteria into 33 distinct categories. To expedite training and enhance classification accuracy, we applied a technique known as transfer learning. This method not only accelerates the training process of the network but also significantly improves its ability to classify bacterial images accurately.

Deep learning, Convolutional Neural Network, ResNet, bacterial classification, Transfer Learning.

"Bacterial Image Classification Using Deep Learning Approach ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.g379-g383, April-2024, Available :http://www.jetir.org/papers/JETIR2404650.pdf

"Bacterial Image Classification Using Deep Learning Approach ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppg379-g383, April-2024, Available at : http://www.jetir.org/papers/JETIR2404650.pdf