MICROORGANISM IMAGE RECOGNITION BASED ON DEEP LEARNING APPLICATIONS

This abstract introduces a Microorganism Image Recognition system leveraging Deep Learning with the LeNet architecture. The study addresses the imperative need for efficient and accurate identification of microorganisms, crucial in various scientific and medical domains. The proposed model utilizes LeNet, a convolutional neural network (CNN) renowned for its effectiveness in image classification tasks. Through extensive experimentation, the system achieves remarkable performance in recognizing diverse microorganisms, surpassing traditional methods. The Deep Learning application is trained on a comprehensive dataset, enabling the model to learn intricate patterns and features inherent in microorganism images. The LeNet architecture's ability to capture hierarchical representations proves instrumental in discerning subtle variations, contributing to heightened recognition accuracy. The system's robustness is evaluated through rigorous testing on a variety of microorganism datasets, showcasing its versatility and applicability across different scenarios. This research advances the field of Microorganism Image Recognition, providing a powerful tool for automated identification and classification, thereby facilitating expedited and accurate analysis in microbiological studies.

Keywords: microorganism recognition, Image processing, Machine learning, Deep learning, Image classification, LeNet.