EARLY DIAGNOSIS OF LUNG CANCER USING MEDICAL IMAGE PROCESSING

Lung cancer seems to be the common cause of death among people throughout the world. Early detection of lung cancer can increase the chance of survival among people. Image processing mechanisms are used in several medical professions for improving the detection of lung cancer. Medical professionals look to time as one of the important parameters to discover cancer in the patient at the earlier stage, which is very important for successful treatment.

This Book focuses on the early diagnosis of pulmonary nodules, and stages of cancer disease appearing in the patient’s lungs. It’s a novel approach to detecting lung cancer from raw chest X-ray images. Hence, a Lung Cancer Diagnosis System using Artificial Neural networks (ANN) to classify lung cancer is developed to detect the accurate size, shape, and location of the pulmonary nodule and to classify the presence of lung cancer in an X-ray image. In this study, MATLABTM has been used through every procedure done. This includes image processing and ANN procedures. It further evaluates for identifying the pixels of an affected nodule. Most of the nodules can be observed after careful selection of parameters like area, perimeter, and shape of the nodule.

The primary objective is to atomize these selections. The database of x-ray images of lung cancer processes in three stages to attain more quality and accuracy in the observational results: image pre-processing, feature extraction, and identification of segmented lung nodules. The proposed approach detected nodules in the diseased area of the lung with accuracy. This system can be modified and developed in a positive direction in the future.