Lung Tumor Detection using Computer Vision: Cheenta Research Program

How AI is Helping Detect Lung Cancer: A Young Researcher's Journey

Artificial intelligence (AI) is making significant advancements in medicine. One young researcher, Rushil Reddy, a 10th grader from Germantown Academy, is contributing to this progress with an AI project designed to improve lung cancer detection. His work earned him second place at the Pennsylvania Junior Academy of Science (PJAS) competition, marking the beginning of an exciting journey.

Why This Project?

Rushil has always been passionate about science, especially medicine, inspired by his father, a doctor. He combined this interest with machine learning to develop a project that enhances lung cancer detection using AI.

Lung cancer remains one of the leading causes of cancer-related deaths in the U.S. Early detection significantly increases survival rates. Doctors typically use CT and PET scans to identify abnormalities. However, determining whether a tumor is cancerous or benign requires time and expertise. Rushil aimed to speed up this process and improve accuracy with AI.

How Does It Work?

Rushil’s project optimizes machine learning models to detect lung tumors. His approach improves the screening process, which involves:

• Initial Checkups – Doctors assess risk factors such as smoking history, age, and family background.
• Medical Imaging – CT or PET scans help detect abnormalities.
• Diagnosis – If a tumor is found, doctors determine if it is cancerous or benign through further tests like biopsies.

Rushil’s AI model automates the final step. Instead of manually analyzing scans, the AI extracts key features and predicts the probability of cancer. This allows doctors to make faster and better-informed decisions.

The AI Model Behind the Project

A key tool in this project is the Brock Model, a widely used predictive model for lung cancer. This model evaluates several factors, including:

• Age and sex
• Family history of cancer
• Nodule size and type
• Nodule location in the lung
• Irregular growth patterns (speculation)

Rushil’s AI model extracts these features from CT and PET scans. It then applies this data to the Brock Model to determine the likelihood of cancer. As a result, doctors can decide whether additional tests, such as a biopsy, are necessary.

Challenges and Future Work

Although the project has already shown promising results, Rushil plans to make further improvements. His next steps include:

• Enhancing tumor segmentation using AI models like Segment Anything Foundation Model.
• Testing different machine learning models to improve accuracy.
• Extracting additional features like speculation and emphysema for more precise predictions.

Beyond building an AI model, Rushil’s work focuses on making AI decisions more transparent for doctors. Many medical professionals hesitate to rely on AI-based diagnoses because they do not fully understand how models reach conclusions. By aligning AI with well-established medical methods, Rushil ensures greater trust in its results.

Final Thoughts

This project highlights how young innovators contribute to medical advancements. Rushil’s work bridges the gap between AI and healthcare, making cancer detection faster and more accessible. As research continues, AI is set to revolutionize early cancer detection and diagnosis.