Utilizing Convolution Neural Networks for enhanced lung cancer classification through CT scan analysis
Date
2024
Authors
Korir, P. J.
Journal Title
Journal ISSN
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Publisher
Strathmore University
Abstract
Lung cancer is the major cause of cancer mortality, which poses significant challenges to accurate and timely diagnosis, especially in resource constrained regions like Kenya. The traditional method of diagnosing lung cancer through Computed Tomography (CT) scans often involves manual interpretation, leading to potential delays and inaccuracies. This research aims to harness the power of Artificial Intelligence (AI) to improve the diagnostic process. This research study developed a Convolution Neural Network (CNN) model for enhanced classification of cancer utilizing CT scan images by fine-tuning the pre-trained ResNet50 architecture. Utilizing Pytorch, a leading deep learning framework for computer vision, the model was trained on a curated dataset from the public Lung Image Database Consortium (LIDC), a medical imaging database for development, training, and evaluation of computer-assisted diagnostic (CAD) methods for lung cancer detection and diagnosis The collected CT scan image include various types of lung cancer, such as adenocarcinoma, squamous cell carcinoma, large cell carcinoma, and normal tissue. Data pre-processing techniques such as resizing, normalization, converting and data augmentation techniques were used to ensure compatibility with the pre-trained model. The model’s performance was evaluated with a range of metrics, demonstrating an accuracy of 87.5%, precision of 80.97%, and an F1 score of 77.4%. These results indicate a promising capability for the model to accurately classify types of lung cancer, supporting its potential use in clinical settings. The pre-trained model was then integrated into a web-based application using the Flask framework, with a frontend designed with Vue.js to provide an intuitive user experience for image upload functionality. The Flask API facilitates communication between the frontend and the ResNet 50-based machine learning model. When a CT scan image is uploaded, it is sent to the Flask backend as an HTTP request. The Flask application processes these requests, extracting the image data and preparing it for analysis by interfacing with the ResNet 50 model, which then classifies the images and retrieves the results.
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Citation
Korir, P. J. (2024). Utilizing Convolution Neural Networks for enhanced lung cancer classification through CT scan analysis [Strathmore University]. http://hdl.handle.net/11071/15648