Data Interpretation and Conclusions: SSD Mobilenet and Faster R-CNN Comparisons

The combined approach of a Faster Regional Based Convolutional Neural Network and a Single Shot Detection Mobilenet architecture are apparent as they offer unique dimensions of accuracy, efficiency, and compatibility in immediate blood-data analysis. Although the Faster R-CNN model revealed minimal converging loss values, a standard of speed and immediate detection was sacrificed. In other words, detection time was extended and required demanding GPU performance to prevent crashing and processing overload. The SSD Mobilenet architecture provided moderate precision-accuracy while providing high speed, less demanding CPU-GPU consumption. With a modified batch size of ~5, the SSD Mobilenet model rapidly passed through global steps reporting decreasing loss values. More importantly SSD Mobilenet created an opportunity for scalability and accessible deployment by adapting to the architectures of small operating systems such as Android and IOS.