Machine Learning approaches for Microscopic Image analysis and Microbial Object Detection(MOD) as a decision support system

Microbes are tiny living organisms beyond the scope to be seen by the naked eye that are coexisting all around the biosphere along with other animals. Significant identification of the microbes from the elementary forms of cell structure to deadly pandemic causing elements are vital for health and hygiene support systems. Manual identification of such creatures consumes infinite amount of time resulting in frequent contamination. In this work, the context is primarily focused on automated microscopic image analysis on visual features by incorporating systematic pattern matching approaches for rapid identification of the microbes. The microbes are classified and recognized using the state of art of real time object detection strategy. Experimental result analysis is done with bacterial images to confirm the precision and accuracy of the utilized technique. Visual and graphical representation of the result obtained confers the validity and correctness of the concerned procedure. Further, the challenges and the difficulties faced during the microbe identification and the techniques to tackle have also been discussed subsequently. The proposed solution is proved to be quick and swift analyzing technique of pathogenic microbes in an organized way and has potential to be used as a source of field-level pathology decision support system.