An Enhancement Of HBNK Algorithm For Prediction Of Lingual And Acrometastasis Using Advanced Communication Channel

Abstract

newline xii newlineABSTRACT newlineLung cancer is a major cause of morbidity and mortality worldwide. Lung newlinecancers may present with metastases in various locations of the body and detection are newlinemade using the symptoms in tongue and hand. Metastasis and Acrometastasis diseases newlinewere presented as the tumor in the tongue and the hands of the human body. newlineAcrometastasis to the hand is an unusual presentation which might mimic an newlineinfectious, inflammatory, or a metabolic pathology. Tongue metastasis is extremely newlinerare as an initial manifestation of the disease. This work proposes Hybrid Bayesian newlinenearest neighbor model classifier (HBNK) to predict the lung cancer using tongue and newlinehand images. newlineSegmentation are performed using Morphological based random walker segmentation newlineand Sobel Edge Detection method. To train the classifier ORB features, color diversity newlinefeatures and the chromatic features are extracted. All the extracted features are loaded newlinein the classification module for further prediction. The Extracted feature values are newlineloaded into the ThingSpeak channel. newlineVisualization tool gets ready to display the data fields of feature extraction values. newlinePutting HBNK on IoT ThingSpeak platform will help doctors to diagnose tongue and newlinehand images that are received from computerized system online. The obtained results newlineof the proposed method are compared with KNN algorithm.