Improving the Accuracy in Lung Cancer Detection Using NN Classifier

Abstract

Lung cancer is a leading cause of cancer-related deaths worldwide, with a high mortality rate and a significant economic burden on health care systems. Traditional screening methods, such as X-rays and CT scans, have limitations in terms of accuracy and efficiency, leading to many cases of lung cancer being diagnosed at a later stage, when treatment options are limited. In this paper, we aim to develop a highly accurate and efficient tool for detecting lung cancer using a NN classifier. We first build a large dataset of medical images and patient data for training and evaluating the NN classifier. The dataset includes a variety of imaging modalities, including CT scans, X-rays, and other medical images. We then develop and train a NN classifier for lung cancer detection, using a deep learning technique. The NN classifiers optimized for high accuracy and efficiency, with the goal of achieving earlier and more accurate diagnosis of lung cancer. We evaluate the performance of the NN classifier using a variety of metrics, including sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC). The classifier is tested on a separate test dataset to ensure that it generalizes well to new data. We also compare the performance of the NN classifier to other traditional screening methods, such as X-rays and CT scans, to determine the potential impact of the NN classifier on lung cancer screening. Finally, we use explainable machine learning technique called as GLCM to identify specific features and patterns in medical images that are indicative of lung cancer. This analysis provides insights into other underlying mechanisms of lung cancer development and may lead to new discoveries and treatment options.