A Correlative Analysis on Machine Learning Algorithms for Prognosis of Lingual Metastasis Using IoT Analytics Platform

Abstract

Recently, image processing techniques are widely used in several medical areas for image improvement in order to provide advanced treatment by detecting the diseases at the earlier stages because time is a very important factor to find out the abnormality issues in the input images, especially in various cancer tumors such as lung cancer, breast cancer, etc. In this paper lung cancer prediction is proposed using Hybrid Bayesian nearest neighbor model classifier (HBNK) classifier with tongue and hand images where metastasis is the tongue based disease and acrometastasis is a hand based disease. The metastasis and acrometastasis is the symptoms of lung cancer. The Image quality and accuracy is the core factors of this research, image quality assessment as well as improvement are depending on the enhancement. The image quality is enhanced using hybrid Weiner-bilateral filter. This work uses the feature extraction techniques like ORB features, color diversity features and chromatic features. Morphological random walker segmentation is introduced to process the tongue segmentation where as sobel edge detection is used to segment hand images. The experimental results are shows that the effectiveness of the proposed method, then the results was analyzed using IOT thingspeak platform which helps the doctors to  access the graphs or images easily at anytime and anywhere.