Classification of Bacteria from Agar Plate Using Deep Learning and Image Processing

This A key component of illness diagnosis is the classification of microorganisms. This is an all-inclusive method for automatically classifying bacteria on agar plates by combining image processing techniques with deep learning methods. Numerous characteristics, including batch size, edges, shape, and color, support the model's categorization and enhance its capacity to discriminate between groups. The research makes use of a unique dataset known as the Agar Dataset, which is spread throughout five different types of bacteria: Pseudomonas aeruginosa (P. aeruginosa), Bacillus subtilis (B. subtilis), Candida albicans (C. albicans), Staphylococcus aureus (S. aureus), and Escherichia coli (E. coli). The suggested model uses a deep convolutional neural network, together with GoogLeNet, AlexNet, VGG-16, SqueezeNet, and DenseNet-161, to extract and learn hierarchical features from photos of bacteria. The most accurate model among the others is GoogLeNet. This signifies a noteworthy progression in the fields of microbiology and laboratory automation. This ground-breaking method uses artificial intelligence to improve and expedite the labor- intensive process of bacterial identification