Detection of Lung Cancer

1. Detection of Lung
Cancer
PRESENTED BY:
207R1A6657-VANCHA VEENA VAHINI
207R1A6641-PESARU NUTHAN
207R1A6652-SOWMITHRI MADHAVA
CHARYA
UNDER THE GUIDE OF:
MR.G.GANGARAM(Assistant professor)
PRESENTED BY:
207R1A6657-VANCHA VEENA VAHINI
207R1A6641-PESARU NUTHAN
207R1A6652-SOWMITHRI MADHAVA
CHARYA
UNDER THE GUIDE OF:
MR.G.GANGARAM(Assistant professor)

2. •Lung cancer is the most important cause of cancer
death for both men and women. Early detection is very
important to enhance a patient’s chance for survival of
lung cancer. This project detects of lung cancer nodules
from the chest Computer Tomography (CT) images.
•They are image pre-processing, extraction of lung
region from chest computer tomography images,
segmentation of lung region, feature extraction from the
segmented region, classification of lung cancer as
benign or malignant.
• Initially total variation based denoising is used for
image denoising, and then segmentation is performed
using optimal thresholding and morphological
operations. Textural features extracted. For
classification, SVM classifier is used.
ABSTRACT:

3. Existing System
•A set of images are utilized in to achieve classification. Here the objective of the
study is to enhance the classification accuracy by applying a hybrid classification
Algorithm. Lung cancer can be detected by image processing.
Disadvantages :
•It sometimes finds abnormalities that turn out not to be cancer hence less accurate.
•Cost effectiveness

4. Proposed System
•In this we use SVM and KMeans algorithm and then comparing
prediction accuracy between them. To implement this we are use lung
cancer images dataset saved inside ‘Dataset’ folder.
•In this dataset we have two types of images such as normal and
abnormal and then SVM and KMEANS will get train on above dataset
and when we upload new image then SVM will predict whether new
image is normal or abnormal.
•The dataset is loaded and now click on ‘Read & Split Dataset to Train
& Test’ button to split dataset into train and test parts and application split
80% dataset for training and 20% dataset to test trained model. The
SVM accuracy is 60% and now click on “Execute K-Means Algorithm”
button to run KMEANS algorithm on loaded dataset. By this we know
svm has better than k means in prediction .
Advantages :
•Lower chance of dying from lung cancer
•High accuracy.

5. System Architecture

6. UML Diagrams
User Case Diagram
Class Diagram

7. Sequence Diagram

8. User Interface Design

9. screenshots

12. REFERENCES
•LIDC-IDRI (Lung Image Database Consortium and Image Database
Resource Initiative)
•Kaggle Data Science Bowl : This dataset contains over 1,400 CT scans
of the chest, with annotations for lung nodules provided by radiologists.
The dataset was used for a Kaggle competition focused on developing
algorithms for automated detection of lung nodules.
•NSCLC Radiomics: This dataset contains CT scans of non-small cell
lung cancer (NSCLC) patients, along with clinical information such as
tumor size, lymph node status, and patient age. The dataset is available
on the Cancer Imaging Archive (TCIA) website.