Abstract: Diagnosis of microbiological and bacterial infections involves extensive procedures ofsample culture and microscopic examination. The diagnosis process begins with the identification of symptoms followed by collection of test samples in the form of blood,scraps of skin lesions, mucus, sputum, urine etc. The entire process of bacteria routine culture and bacteriological examination may take as long as 10 to 11 days. Due to the long time required for the standard process of species identification, high costs and need of human expertise it is beneficial to use methods that do not rely on conventional methods. In this work we propose a comprehensive Deep Learning basedapproach to identify urinary Tract Infection (UTI) causing bacteria from microbial colony images of bacteria cultured on agar plates. The approach explains the use of VGG-19 and Inception-v3 deep learning architectures, in bacteria identification evaluated on The Annotated Germs for Automated Recognition (AGAR) dataset, an image dataset of microbial colonies cultured on agar plates. The findings affirmed the significant potential of employing deep learning techniques for the identification of microbial colonies and their classification using Petri dish images.
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