Detecting Lung Cancer Using Machine Learning Techniques

In recent days, Internet of Things (IoT) based image classification technique in the healthcare services is becoming a familiar concept that supports process detecting cancers with Computer Tomography (CT) images. Lung cancer one perilous diseases increases mortality rate exponentially. IoT classifiers have ability to detect at an early stage and life span patient. It oncologist monitor evaluate health condition Also, it can decipher risk marker act upon them. The feature extraction selection from CT images plays key role identifying hot spots. Convolutional Neural Network (CNN) efficient techniques improves performance classifier by reducing entropy data sets. A Random Forest (RF) machine learning improve its efficiency support CNN. This paper presents RF CNN percentage accuracy spots experimentation proposed approach on three dimensions: Feature extraction, selection, prediction To approach, benchmark repositories which consists 3954 50 low dose whole lungs scan are employed. method achieves effective result all test under different aspects. Consequently, obtains average 93.25% F-measure 91.75% higher than other methods, comparatively.