Lung Cancer Detection using Efficient Net - B0
Year: 2025
Type: article
Abstract: Lung cancer remains a leading cause of cancer-related deaths worldwide, with early detection being crucial for improving survival rates. Traditional diagnostic approaches rely on manual assessment by radiologists, which can be time-consuming and prone to human error. This paper presents an AI-powered Lung Cancer Detection System utilizing deep learning models, specifically EfficientNet-B0, to classify lung CT scans into different stages. The system is designed with an interactive user interface built using Streamlit and performs model inference using PyTorch. Advanced image preprocessing techniques, including resizing and normalization, enhance prediction robustness. The model is evaluated based on accuracy, precision, recall, and confusion matrix analysis. Our findings suggest that this AI-based solution can significantly aid in the early detection of lung cancer, reducing the manual diagnostic workload and improving patient outcomes.
Authors: Vishwa Chandra Jajula, Akshay Maddala, Veera Venkat Narayana Kotha, Sasi Kumar Medabalimi, Nancy Anurag P
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W6911965416
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