Lung cancer is a major cause of rising death rates around the world. Finding it early can help people survive longer. In the last five years, early diagnosis has increased the survival rate from 14% to 49%. Computed Tomography (CT) scans are good at finding lung cancer. Using different image processing methods with CT scans helps in early detection. This paper talks about image processing techniques like image enhancement, segmentation, and feature extraction used to detect lung cancer. The algorithms tested include CNN, binary segmentation, and Otsu segmentation.
Existing Systemin This Existing System The current system uses three methods: K-means clustering, Wavelet and Principal Component Analysis, and the KNN classifier. K-means clustering is a way to group data without labels. It finds K groups in the data, where K is a number you choose. The algorithm works by assigning each data point to one of K groups based on their features. It keeps refining the groups until the data points do not change groups anymore or until it reaches the maximum number of tries. The main results are the centers of each group (called centroids) and the group labels for each data point.
To choose the best number of groups (K), you can try different K values and compare the results. One way to do this is by looking for the elbow point in a graph, which shows the change in distance between data points and their centroids. Other methods include cross-validation and the silhouette method. An adaptive version of K-means clustering can improve accuracy by creating and merging clusters as needed, which makes the process faster and less dependent on initial choices. However, K-means clustering can sometimes give inaccurate results, has poor discrimination power, and works less well for segmenting multiple images quickly.
Proposed Systemin This Proposed System First, we prepare the image by cleaning and enhancing it, which is called preprocessing. Next, we use a method called Discrete Wavelet Transform (DWT) to break the image into smaller parts. Then, we use Gray Level Co-occurrence Matrix (GLCM) to analyze the texture by looking at how often pairs of pixels with specific values occur. Finally, we use a Convolutional Neural Network (CNN) to classify the image. CNNs are powerful tools inspired by how our brain processes visual information. They look for patterns like edges and curves to recognize objects in the image. This combination of steps makes the process effective and helps in better understanding and classifying images.
Block Diagram
This project is about using a deep learning model to help doctors find lung cancer in CT images. We take pictures of the lungs and use a special computer program to look at these images. The program is trained to spot cancer by looking for tiny changes in the pictures. It works like a smart assistant that can help doctors see if there is cancer in the lungs more quickly and accurately. This can help in treating the cancer early and save lives.
Process ( Training And Testing)
Input Image with DWT Method And Detection of Stage
Segmentation Process And Result Analysis
Segmentation Process And Result Analysis
Detection Of Cancer Normal
ADVANTAGES1.Simple and computationally fast.
2.Segments multiple regions at the same time.
3.It is simple and powerful.
1.Agriculture fields.
2.Bio-Medical Aplications.
3.Signature Verification.
Frequently Asked Questions (FAQs)
1. What is this project about?
This project uses a deep learning model to identify and segment lung cancer in CT images. It helps doctors detect cancer early and accurately.
2. What are CT images?
CT (Computed Tomography) images are detailed pictures of the inside of the body taken using X-rays and a computer. They are often used to look for lung cancer.
3. How does the deep learning model work?
The model is trained with many CT images to recognize patterns and features of lung cancer. It can then analyze new images to detect and outline cancerous areas.
4. Why is this important?
Early and accurate detection of lung cancer can lead to better treatment outcomes and can save lives.
5. Who can use this model?
Doctors and medical professionals can use this model to assist in diagnosing lung cancer from CT scans.
6. How accurate is the model?
The accuracy depends on the quality and quantity of the training data, but deep learning models generally provide high accuracy in detecting and segmenting cancer.
7. Is this model a replacement for doctors?
No, it is not a replacement. It is a tool to assist doctors in making more accurate and quicker diagnoses.
8. How can this model benefit patients?
By helping to detect lung cancer early, patients can receive timely treatment, which can improve their chances of recovery.