Novel convolutional neural network for bacterial identification of confocal microscopic datasets

Abstract

Abstract Artificial intelligence (AI), complex mathematical algorithms, is currently employed across various fields to perform tasks quickly and effectively. In this study, a novel deep-learning algorithm named (CM-Net) was developed to classify biological data obtained as images from Confocal Microscopy. The images were collected for two types of bacterial species: ( Escherichia coli and Staphylococcus aureus ), where the number of images was 300 for each class. To enhance the dataset, we divided each image (using the augmentation method) into a small number of images with 224 × 224 dimensions, resulting in a total of 7066 images for both classes. These augmented images were fed to CM-Net to ensure accurate results and avoid bias in the developed algorithms. The algorithm was trained and tested 30 times with a 5-K cross-validation for each time. The algorithm’s performance was evaluated using seven metrics (accuracy, sensitivity, specificity, precision, NVA, F1-score, and MCC), where the respective results were 96.08%, 95.98%, 96.19%, 96.78%, 95.26%, 96.38%, and 92.11%, indicating the model’s high accuracy and reliability. CM-Net drastically reduces bacterial identification time by automating large-scale data analysis, processing results in 8.9 min. The automation provided by CM-Net simplifies workflows, enabling non-expert workers to perform microbial identification without extensive training. The significant outcomes of applying CM-Net for bacterial identification revolve around its transformative impact on data analysis’s speed, efficiency, and accuracy, making advanced analysis accessible to non-experts while minimizing human error.