EXPERT DIAGNOSIS SYSTEM FOR BACTERIA CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK (CNN)

ABSTRACT Bacterial infections pose a significant threat to public health, necessitating accurate and timely identification of bacterial pathogens. Traditional methods for bacterial classification, such as culturing and biochemical tests, are labor-intensive, time-consuming, and prone to human error. Recent advancements in deep learning, particularly Convolutional Neural Networks (CNNs), offer a promising solution to these challenges. This research presents the development of an automated expert diagnosis system for bacterial classification using CNNs, aimed at improving diagnostic accuracy and efficiency. The system was designed to classify microscopic images of bacteria into distinct categories based on their morphological features. The CNN model was trained and validated using the Digital Images of Bacteria Species (DIBaS) dataset, which includes images of 33 bacterial species. The model's performance was assessed using metrics such as accuracy, sensitivity, specificity, and computational efficiency. The results demonstrate that the CNN-based system achieved high classification accuracy, outperforming traditional methods in both speed and reliability.The expert diagnosis system was implemented as a software application, providing a user-friendly interface for real-time bacterial identification. This application has the potential to significantly enhance diagnostic workflows in clinical, environmental, and industrial settings by automating the bacterial classification process, reducing reliance on expert interpretation, and minimizing the time required for accurate diagnosis.