Lung cancer detection using RF-K-means and classification with optimized ANN algorithm

Abstract

Lung cancer is one of the leading causes of mortality from cancer. Lung cancer is a kind of malignant lung tumor characterized by uncontrolled cell proliferation in lung tissues. Even though CT scans are the most often used imaging technology in medicine, clinicians find it challenging to interpret and diagnose cancer from CT scan pictures. As a result, computer-aided diagnostics can assist clinicians in precisely identifying malignant cells. Many computer-aided approaches were explored and applied, including image processing and machine learning. A comparison of the various classification methodologies will assist in enhancing the accuracy of lung cancer detection systems that employ robust segmentation and classification algorithms presented in this research. This research proposed to enhance existing segmentation and classification-basedmethodsof human lung cancer detection with optimization in techniques. The workflow includes initial preprocessing of medical images, for segmentation a novel hybrid methodology is developed by combining enhanced k-means clustering and random forest and classification with an Artificial neural network enhanced with PSO parameter and feature optimization.