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Measles disease is usually diagnosed through an RT-PCR test, which detects the measles-specific IgM antibody in serum samples. However, this approach is highly expensive and requires a lot of sample preprocessing making it a laborious task. Another approach for measles identification could be a cytological smear test -- which is a low-cost, quick, and accurate method for the early detection of measles disease. Due to the lack of experience in interpreting smears, its usage in clinical practice is currently limited. We have designed a MesoSpot, an automatic segmentation method for the first time that can precisely define the region of giant cells in nasal aspirate cytological smears and assist medical experts in the speedy diagnosis of measles disease. MesoSpot was developed by training 500 cytological smear images collected from various web sources. We have used 50 cytological smear images to validate the model and evaluated its performance using various metrics. The main purpose of the proposed study is to detect multinucleated giant cells and estimate their size and number, which aids in assessing the disease's severity. To annotate the giant cells in each cytological smear, we have applied a semantic segmentation approach using APEER software. UNET architecture was used to train the model, and image processing techniques were used to spot giant cells and predict their size. Various performance metrics were employed to assess the model's efficiency. The model's output was also compared to the ground truth image and statistically confirmed. [ABSTRACT FROM AUTHOR]

The manual selection of transformed bacterial colonies from non-transformed ones grown on agar plate post-blue-white screening -- despite chromogenic difference -- is cumbersome, owing to their small size and large cell number. The present study offers a lucrative solution to this problem by the design of an automated system that is not only fast and less laborious but also low priced and user-friendly. The image masking technique was used to distinguish plated transformed colonies. This method uses computer vision techniques to detect the number of blue and white colonies post bacterial transformation on agar plates and calculate the transformation efficiency. To assess the proposed model with the manual counting method, we have validated the model by comparing the manual counting of colonies with the automated system count. Hence, a model was developed that would be an added advantage to biotechnologists as it would minimize the time required for counting and help in productive research. [ABSTRACT FROM AUTHOR]