MICROORGANISM IMAGE RECOGNITION

Efficient identification of microorganisms from microscopic imagery is essential for applications in clinicaldiagnostics, water quality monitoring, and biomedical research. Manual examination, while common, is laborintensive and prone to inconsistencies. This paper introduces an automated detection model employing the Single ShotMultiBox Detector (SSD) a real-time object detection algorithm to locate and classify multiple types ofmicroorganisms within microscopic images. Utilizing a convolutional neural network backbone, the model learnsspatial hierarchies of features and performs both localization and classification simultaneously. A curated andannotated dataset of microorganism images is used to train the model, with performance evaluated using standarddetection metrics. The proposed SSD-based architecture demonstrates robust accuracy, fast inference, and reliabilityacross multiple microorganism categories, showcasing its potential as a valuable tool in laboratory automation anddigital pathology.