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Automated Diagnosis of Lung Cancer Using CT Scans

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Introduction

Lung cancer remains one of the leading causes of cancer-related deaths worldwide. Early and accurate detection is crucial for improving patient outcomes. This project proposal focuses on developing an automated system for diagnosing lung cancer using CT scans, inspired by recent advancements in deep learning and medical imaging.

Background

Recent research has demonstrated the potential of deep learning models in enhancing the accuracy of medical diagnoses from imaging data. Convolutional Neural Networks (CNNs) have been particularly effective in image classification tasks, making them suitable for analyzing complex medical images such as CT scans. By automating the diagnostic process, these models can assist radiologists in identifying and classifying lung cancer more efficiently.

Project Objective

The primary objective of this project is to develop a robust automated diagnostic system for lung cancer using CT scans. The system aims to accurately classify lung nodules as benign or malignant, thereby assisting in early detection and treatment planning.

Methodology

1. Data Collection and Preprocessing

  • Datasets: Utilize the IQ-OTH/NCCD lung cancer dataset, which includes 1190 CT scan images categorized into normal, benign, and malignant cases[2].
  • Preprocessing: Convert DICOM images to a suitable format for analysis, normalize pixel values, and segment lung regions to focus on areas of interest.

2. Model Architecture

  • Convolutional Neural Network (CNN): Develop a CNN model to extract relevant features from CT scan images.
  • Transfer Learning: Implement transfer learning techniques using pre-trained models like VGG16 or ResNet to improve performance with limited data.

3. Training and Evaluation

  • Training: Train the model using a labeled dataset with cross-validation to ensure robustness.
  • Evaluation Metrics: Assess model performance using metrics such as accuracy, sensitivity, specificity, and area under the ROC curve (AUC).

Expected Outcomes

The proposed system is expected to achieve high accuracy in classifying lung nodules, reducing false positives and negatives compared to traditional methods. By leveraging deep learning techniques, the system should provide reliable assistance in clinical settings.

Conclusion

This project aims to advance the field of medical imaging by developing an automated diagnostic tool for lung cancer detection using CT scans. The integration of CNNs and transfer learning is anticipated to enhance diagnostic accuracy and support radiologists in making informed decisions.

For further details on related research, please refer to the paper "Automated Diagnosis of Lung Cancer Using CT Scans" available at https://ieeexplore.ieee.org/document/8489099.

Dataset link: IQ-OTH/NCCD Lung Cancer Dataset

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