Potential Diagnosis of Cancerous Cells Through Utilising Optical Spectroscopy
Cancer is responsible for an estimated 9.6 million deaths in 2018. Deaths from cancer worldwide are projected to reach over 13 million in 2030. Thus, developing a device that has the capability to solve today’s toughest global challenge is crucial by utilizing a simple yet robust approach - “SEEING THE UNSEEABLE” through bold innovation. Although removing cancer is much more effective than either radiation or chemotherapy, when unseen residual cancer cells remain, they could grow back into tumour overtime. The reoccurrence of cancer contributes to a greater risk of death. Hence, launching a system that is able to distinguish between the cancerous cell and normal cell is ultimately essential to make sure no cancer is left behind during surgery. This robust optical system is established with quantitative approach by exploring the integration of an algorithm into the developed software. The end result of this device has the capability to provide users an accurate numerical pH value. The developed system is integrated with the smart IoT gateway capability whereby this powerful analytical device is incorporated with the real-time monitoring, data transformation and data analyzer. Harnessing the power of technology lets us fight cancer better. Each time a pathologist analyzes tissue after operation, it can take up 2 to 3 days because the tissue has to be frozen, thinly sliced, and stained so it can be viewed under the microscope during the process of biopsy. Thus, it is crucial to invent this Surgeons’ VisionMetric device which has an IoT-based microcontroller that is capable of providing real-time numerical value on-site.
DetectTimely
This research project focuses on developing a web-based multi-platform solution for augmenting prognostic strategies to diagnose breast cancer (BC), from a variety of different tests, including histology, mammography, cytopathology, and fine-needle aspiration cytology, all in an automated fashion. The respective application utilizes tensor-based data representations and deep learning architectural algorithms, to produce optimized models for the prediction of novel instances against each of these medical tests. This system has been designed in a way that all of its computation can be integrated seamlessly into a clinical setting, without posing any disruption to a clinician’s productivity or workflow, but rather an enhancement of their capabilities. This software can make the diagnostic process automated, standardized, faster, and even more accurate than current benchmarks achieved by both pathologists, and radiologists, which makes it invaluable from a clinical standpoint to make well-informed diagnostic decisions with nominal resources.