Introduction
This medical object detection project explores and compares the benefits and costs in utilizing the SSD (Single Shot Detector) MobileNet and the Faster R-CNN Tensorflow Models as it pertains to detecting blood-borne pathogens- specifically Malaria and Syphilis in dark field microscopy images. Further, the paper will analyze the real-world practicality of utilizing both of these models in object detection challenges in addition to both models’ capacities in detecting sample slides from Malaria and Syphilis cultures. The authors propose a cost-benefit system to assess the performance of two distinct models in various settings. The Faster R-CNN model’s training process requires about half the time as needed to create a checkpoints and detect loss values in the images versus the SSD MobileNet architecture. In addition, Faster-RCNN marks a higher accuracy in detecting a greater number of cells as opposed to the SSD MobileNet. Although an assessment of precision and accuracy is required in both API models, the practical real time nature of the framework must be considered by observing the graphical compactness, efficiency, and lightness of each model in a limited environment (i.e. 3rd world setting). The communicability and variance of infectious diseases, such as Malaria and Syphilis, create a public health problem by making onsite medical intervention difficult. Further, the microheterogeneity of Malaria complicates the process of rapid human-controlled pathology diagnoses in local clinical laboratories.
The infeasibility of “human eye” feature extraction alone is apparent as a full determination and processing of sample information can require up to 7 days in the case of unexpected complications in tissue-blood analysis. Although existing feature segmentation techniques facilitate the visualization of histology samples, there is a growing gap in diagnosis due to the lack of specificity and machine-confirmed confidence/percentile scores. In the scope of a limited 3rd world environment, the ideal of accessibility and functionality is questioned in models such as Faster RCNN Inception v2 and SSD Mobilenet v1 due to their inherent graphical capabilities in FPS rate and real time motion detection. Utilizing a deployable object detection model that can be integrated into common IoT (Internet of Things) or system architectures would minimize the accessibility gap for a multi diagnostic app. However, the concept of a Cognitive Domain in the pathology treatment process must be attained by preserving detection accuracy, specificity, and sensitivity.
GPU (Graphical Processing Unit) tests on the R-CNN and Mobilenet Tensorflow models reveal distinct parameters and characteristics: Faster R-CNN preserves suitable precision and accuracy, however, demands more processing time whilst operating at a low FPS rate, making real time object detection difficult. The SSD Mobilenet architecture (v1-v2) creates an opportunity for moderate accuracy and average precision coupled with high scalability, FPS rates, real time capabilities, and low processing times. The SSD Mobilenet versions-series offers high deployability on low-CPU/GPU graded devices, including smartphones, Raspberry pies, and other other low-performance motherboards and computers by offering minimal overconsumption in the image processing procedure. Common operating system platforms including Android and IOS function with the Mobilenet architecture due to high scalability and compactness in CPU consumption. Although the pretrained Faster R-CNN Inception model offers critical image detection accuracies, a combination of low scalability, inaccessibility, and graphics limitations creates an unpromising use of the model. The SSD Mobilnet architecture demands additional training to suffice the loss-accuracy values of the R-CNN model, however, offers practicality, scalability, and easy accessibility on smaller devices which reveals the SSD model as a promising candidate for further assessment.
Pathology Diagnosis: The Cognitive Domain
The cognitive domain establishes the analytical process in a pathology diagnosis through a systematic walkthrough of various strategies and tools in the collection of data and the manipulation of clinical-microscopy findings. The strategies allow a deduction of biological and clinical data to determine objective solutions. However, an overarching mechanism, known as metacognition, is exerted over these hypotheses and is checked/evaluated in relation to the data collection. Metacognition is defined as a direct-active control over one’s thought process (Pena & Andrade-Fiho, 2009). Although the Cognitive Domain is a clinical laboratory ideal, human performance complications can cause missed details during analysis and confirmation. More importantly, personal biases during the pathology investigations can cause low specificity and false-positives or false-positives during the diagnostics process. Integrating an ML approach ensures that specificity and biases are continuously regulated in the training process by using constant hyperparameters that prevent anomalies and complications during the machine evaluation. The traditional Cognitive Domain is demonstrated in figure 1.