DETECTION OF LUNG CANCER USING DEEP LEARNING
Creators: name: Rangarajan, Dinesh kumar
Publisher: name: figshare
Year: 2023
Subjects: Health informatics and information systems

description: Lung cancer is one of the leading causes of mortality in every country, affecting both men and women. Lung cancer has a low prognosis, resulting in a high death rate. The computing sector is fully automating it, and the medical industry is also automating itself with the aid of image recognition and data analytics. Lung cancer is one of the most common diseases for human beings everywhere throughout the world. Lung cancer is a disease which arises due to growth of unwanted tissues in the lung and this growth which spreads beyond the lung are named as metastasis which spreads into other parts of the body. The objective of our project is to inspect accuracy ratio of two classifiers which is Support Vector Machine (SVM), and K Nearest Neighbour (KNN) on common platform that classify lung cancer in early stage so that many lives can be saving. The experimental results show that KNN gives the best result Than SVM. This report discusses the Implementation details of our project. We have done data preprocessing, data cleaning and implements machine leaning algorithm for prediction of lung cancer at early stages through their symptoms. We have used both classification algorithms to find or predict the accuracy ratio. Lung cancer is identified as the most common cancer in the world that causes death. Early detection has the ability to reduce deaths by 20%. In the current clinical process, radiologists use Computed Tomography (CT) scans to identify lung cancer in early stages. Radiologists do so by searching for regions called ‘nodules’, which correspond to abnormal cell growths. But identifying process is time consuming, laborious and depends on the experience of the radiologist. Hence an intelligent system to automatically assess whether a patient is prone to have a lung cancer is a need. This paper presents a novel method which use deep learning, namely convolutional neural networks (CNNs) to identify whether a given CT scan shows evidence of lung cancer or not. The implementation uses a combination of classical feature-based candidate detection with modern deep-learning architectures to generate excellent results better than either of the methods. The overall implementation consists of two stages. Nodule Regions-of-Interest (ROI) extraction and cancer classification. In nodule ROI extraction stage, we select top most candidate regions as nodules. A combination of rule based image processing method and a 2D CNN was used for this stage. In the cancer classification stage, we estimate the malignancy of each nodule regions and hence label the whole CT scan as cancerous or non-cancerous. A combination of feature based eXtreme Gradient Boosting (XGBoost) classifier and 3D CNN was used for this stage. The LUNA dataset and LIDC dataset were used for both training and testing. The results were clearly demonstrated promising classification performance. The sensitivity, accuracy and specificity values obtained for the nodule ROI segmentation and cancer classification showed to be improved in the combined approach of deep learning with classical feature based classifiers compared to the deep-learning only techniques.