Lung Cancer Prediction and Detection Using Image Processing Mechanisms: An Overview

Abstract

Nowadays, cancer has counted as a hazardous disease that many people suffered from especially Lung-Cancer. Cancer is the disease that cell has grown rapidly and abnormally that is why treating it is somehow tough in some cases but it can be controlled if it is detected in the initial stage. Image Processing Mechanisms have a vital role in predicting and recognizing both benign and malignant cells with the help of classifier mechanisms such as Decision-Tree (D-Tree), A-NN, Support-Vector-Machine, and Naïve-Bayes classifier which are widely utilized in the biomedical field. These classifiers are available to classify the usual and unusual cells. This study aims to review the most well-known Image Processing Mechanisms for Lung-Cancer Detection and Prediction. Brief information about the main steps of proposing an effective system by using Image Processing stages like Image Acquisition, Pre-processing of the image which includes noise elimination and enhancement, Segmentation, Extracting Feature, and Binarization had been demonstrated. In the literature, several researchers' work had been reviewed. A comparison had been done among various reviewed research papers that proposed various models for recognizing and estimating the Lung-Cancer nodule. The comparison based on the Image Processing Mechanisms, accuracy, and classifier used in each reviewed research paper.