Identification of Lung Cancer on Chest X-Ray (CXR) Medical Images Using the Probabilistic Neural Network Method

Abstract

The high death rate from lung cancer is caused by the late detection or detection of cancer in the body so that treatment is carried out after the cancer has entered a high stage of severity. Meanwhile, lung cancer is a disease that requires fast and targeted treatment. One of the diagnoses of lung cancer can be done through a chest X-ray (CXR) and the results can only be read by an expert such as a doctor. In many cases, the doctor's diagnosis takes time and is still possible for errors. With increasingly sophisticated knowledge, several diseases can be identified by image analysis, including lung cancer. Digital image processing and methods and neural networks can help identify cancer. The method proposed in this study is a probabilistic neural network to identify normal and cancer images from lung images in lung cancer detection. The identification stage consists of several stages, namely preprocessing, segmentation with canny edge detection, feature extraction with the Gray Level Co-Ocorrence Matrix (GLCM) and identification with a Probabilistic Neural Network (PNN). With the method used, the accuracy of the lung cancer identification results was 93.33%.