Browsing by Subject "CNN"

Immunohistochemistry (IHC) is a widely used research tool for detecting antigens and can be used in medical and biochemical research. The co-localization of two separate proteins is sometimes crucial for analysis, requiring a double staining. This comes with a number of challenges since staining results depend on the pre-treatment of samples, host-species where the antibody was raised and spectral differentiation of the two proteins. In this study, the proteins GABAR-α2 and CAMKII were stained simultaneously to study the expression of the GABA receptor in hippocampal pyramidal cells. This was performed in PGC-1α transgenic mice, possibly expressing GABAR-α2 excessively compared to wildtype mice. Staining optimization was performed regarding primary and secondary antibody concentration, section thickness, antigen retrieval and detergent. Double staining was performed successfully and proteins of interest were visualized using a confocal microscope after which image analyses were performed using two different methods: 1) a traditional image analysis based on intensity and density of stained dots and 2) a novel convolutional neural network (CNN) machine learning approach. The traditional image analysis did not detect any differences in the stained brain slices, whereas the CNN model showed an accuracy of 72% in categorizing the images correctly as transgenic/wildtype brain slices. The results from the CNN model imply that GABAR-α2 is expressed differently in PGC-1α transgenic mice, which might impact other factors such as behaviour and learning. This protocol and the novel method of using CNN as an image analysis tool can be of future help when performing IHC analysis on brain neuronal studies.

Insect pests substantially impact global agriculture, and pest control is essential for global food production. However, some pest control measures, such as intensive insecticide use, can have adverse ecological and economic effects. Consequently, there is a growing need for advanced pest management tools that can be integrated into intelligent farming strategies and precision agriculture. This study explores the potential of a machine learning tool to automatically identify and quantify fruit fly pests from images in the context of Ghanaian mango orchards in West Africa. Fruit flies provide a special challenge for computer vision-based deep learning due to their small size and taxonomic diversity. Insects were captured using sticky traps together with attractant pheromones. The traps were then photographed in the field using regular smartphone cameras. The image data contained 1434 examples of the targeted pests, and it was used to train a convolutional neural network model (CNN) for counting and classifying the fruit flies into two different genera: Bactrocera and Ceratits. High-resolution images were used to train the YOLOv7 object detection algorithm. The training involved manual hyper-parameter optimization emphasizing pre-selected hyper parameters. The focus was on employing appropriate evaluation metrics during model training. The final model had a mean average precision (mAP) of 0.746 and was able to identify 82% of the Ceratitis and 70% of the Bactrocera examples in the validation data. Results promote the advantages of a computer vision-based solution for automated multi-class insect identification and counting. Low-effort data collection using smartphones is sufficient to train a modern CNN model efficiently, even with a limited number of field images. Further research is needed to effectively integrate this technology into decision-making systems for pre cision agriculture in tropical Africa. Nevertheless, this work serves as a proof of concept, show casing the serious potential of computer vision-based models in automated or semi-automated pest monitoring. Such models can enable new strategies for monitoring pest populations and targeting pest control methods. The same technology has potential not only in agriculture but in insect monitoring in general.