Early detection of Cancer can save not only money but also countless lives.
With various fields of Medical Science using advanced technology like Artificial Intelligence (AI) to treat terminal and previously irreversible diseases, technology has come has come a long way. Particularly, the use of Deep Learning- a technology used under AI- has spiked in the field of Medical Science in last couple of years. Deep Learning techniques have been instrumental in examining series of images and identify diseases with rich insights including early detection, treatment planning, and disease monitoring. Notably, with the predominant usage in Oncology to detect cancer, Deep Learning has earned its place in Medical Science.
A recent study by NVIDIA- a leading American visual computing technologies firm- shows that the rate of wrongful breast cancer diagnoses has dropped by 85% with the use of Deep Learning methods. Such is the impact in the practical scenarios of disease diagnosis. Let’s find out how Deep Learning helps cut down false detection rate.
Using Convolutional Neural Networks (CNN) - a Deep Learning technique- the chances of Cancer detection are increased. CNN, which is a category of Neutral Networks, proves its effectiveness due to the presence of one or more convolutional layers thus helping in 2D structure of an input image.
The system can also judge the malignancy of nodules and detects the cancerous nodule with a high accuracy rate of 85.33%. The algorithms developed have been able to accurately determine the cancerous lung lesions while reducing the False Positive Rate (FPR) dramatically. In order to run the algorithm, the Sobel-Gradient-Image of the original scan is used to calculate the edge detection. Using features extraction methodology, the desired portions are isolated. Using this data, the detection of cancer was done with either of the two approaches namely Binarization approach and Masking approach.
It’d not be wrong to claim that Deep Learning techniques has the power to examine hundreds of images to identify different types of diseases while providing rich insights into multiple areas including early detection, treatment planning, and disease monitoring. The system is also highly compatible with existing health systems to create integrated solution based on multiple sources of data. Using such high performing systems, the post-analysis of doctors can increase the accuracy of cancer detection.