Future Applications
The broad constraints of a darkfield-based object detection application were sufficed in terms of moderate accuracy and compactibility in the SSD Mobilenet model. More importantly, the potential of Android-OS device deployment of the SSD Mobilenet architecture enables high accessibility and versatility on other cross-platforms. Moreover, the compressed nature of the SSD Mobilenet architecture allows precise object tracking and more rapid image analysis/processing time. In contrast to the Faster R-CNN model, single shot detection offers high general scalability. Primary future developments suggest that the SSD Mobilenet model may be integrated into a full scale application for multiple diagnostic disease detection. Additionally, to equate precision and accuracy readings with the Faster R-CNN model, continued GPU training is necessary. This project was completed on a local network with insufficient graphics support, however, future work in a high-performance research setting with necessary computational-graphics resources is required for considerable improvement in detection efficacy. Considering the potential of medical visualization in the process of diagnostics reports, cell segmentation techniques plan on being applied using an extension of the Faster R-CNN architecture known as “Mask-R-CNN” which applies regional “masks” and highlights critical areas of the image based on borderline features.
Bibliography
Dark field microscopy. (2011). Paracelsus Clinica,1-2. Retrieved July 3, 2018.
Foundation, A. M. (2008). The burden of malaria in Africa. 1-7. Retrieved July 3, 2018.
Howard, A. G. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. Arxiv: Computer Vision and Pattern Recognition,1-9. Retrieved July 3, 2018.
Jha, P. (2013). Disease Control Priorities in Developing Countries, 3rd Edition Working Paper #2. Economic Evaluation for Health,1-66. Retrieved July 3, 2018.
Johnson, J. (n.d.). Convolution Neural Networks for Visual Recognition. Retrieved July 3, 2018, from
http://cs231n.github.io/Kennedy, E. J., Jr. (1996). DARKFIELD MICROSCOPY FOR THE DETECTION AND IDENTIFICATION OF TREPONEMA PALLIDUM. 1-18. Retrieved July 3, 2018.
Liu, W. (2015). SSD: Single Shot MultiBox Detector. Arxiv: Computer Vision and Pattern Recognition,1-17. doi:10.1007/978-3-319-46448-0_2
Mogeni, P., & Williams, T. (2017). Detecting Malaria Hotspots: A Comparison of Rapid Diagnostic Test, Microscopy, and Polymerase Chain Reaction. The Journal of Infectious Diseases,216(9), 27th ser., 1091-1098. Retrieved July 3, 2018.
Mustamo, P. (2018). Object detection in sports: TensorFlow Object Detection API case study. University of Oulu: Faculty of Science,1-43. Retrieved July 3, 2018.
Organization, W. H. (2016). Multiplexed Point-of-Care test for acute febrile illness (mPOCT). 1-2. Retrieved July 3, 2018.
Pena, G. P., & Andrade-Filho, ;. (2009). How Does a Pathologist Make a Diagnosis? Archives of Pathology and Laboratory Medicine and Archives of Pathology,1-9. Retrieved July 3, 2018.
Regazzi Avelleira, J., & Bottino, G. (2006). Syphilis: Diagnosis, treatment and control. Continuing Medical Education,111-122. Retrieved July 3, 2018.
Ren, S., He, K., Girshick, R., & Sun, J. (2016). Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. 1-14. Retrieved July 3, 2018.
Xu, J. (2017). Deep Learning for Object Detection: A Comprehensive Review. Towards Data Science. Retrieved July 3, 2018, from https://towardsdatascience.com/deep-learning-for-object-detection-a-comprehensive-review-73930816d8d9.