Deep- Image: Automated Identification of Bacteria based on Deep Learning Model - Al-Qudsy, Zainab N.; Alaluosi, Wasan Maddah; Mijwil, Maad M.; Hadi, Ahmed Adnan; Aljanabi, Mohammad (Journal of Advanced Research Design, 2025)
Accurate Classification of bacteria plays a crucial role in microbiology and beyond. It helps to identify infectious agents during epidemiological investigations, food safety monitoring, and detection of biological threat agents. Convolutional Neural Network (CNN) is a deep learning technique that has proven reliable in the field of Classification of medical and biological diseases. In this study, CNNs are utilized to develop a bacterial classification system. Within this system, Classification is subjected to several modifications before the ResNet method is used in order to identify the kinds of Bactria from among sixteen different classes of bacterial images. The model was fine-tuned by training only the last two layers of the pre-trained ResNet101V2 network, which significantly improved the performance. A large-scale dataset and confusion matrix were used to evaluate the model’s performance. The experimental results demonstrate that the accuracy rate reached a peak of 98.66%. Moreover, the suggested approach enhances the advancement of automated diagnostic tools for bacterial pictures that surpass the present state-of-the-art models and provide the groundwork for future enhancements in bacterial image classification utilizing CNNs.