A Novel Approach Using AI-vision Transformer for CT Scan Analysis for Lung Cancer Detection

Summary

The use of Vision Transformers in this study allows lung cancer to be classified from CT images, achieving an accuracy of 98.18%, at 1,190 scans. The model preserved a satisfactory level of robustness, with accuracy levels between (80–88%) with noisy and blurred images, against standard convolutional neural network (CNN) models, in consumer health diagnostic applications.

Abstract

Diagnosing lung cancer remains challenging because of the subtle differences between benign versus malignant disease on imaging studies. While traditional, convolutional neural networks (CNNs) have been the mainstay of medical imaging analysis, they still have limited applicability and robustness with variation of imaging methods. The advancement of machine learning is transforming traditional methods of diagnosis in consumer-facing healthcare technologies, changing processes in accessibility and personalized patient care. This paper describes the use of Vision Transformers (ViTs) for the task of lung cancer classification with CT scans, associated with consumer health applications. The Vision Transformer model was trained on complete dataset of 1,190 CT images, and achieved a classification rate of 98.18% with a Matthews Correlation Coefficient of 0.9676. Additionally, the model's performance was validated on simulated real-time scenarios using real-time noises and blurred datasets. Although preserving validation methods with strong diagnostic accuracy, across the entire dataset the ViT model also outperformed the conventional validated method by showing an accuracy of between 80-88% to differentiate between the noisy images and fuzzy images, even under these circumstances. While initial results provide promising information about the robustness of ViTs when dealing with image variation, a cautious approach should still be exercised in contextualizing results since the evaluation studies were completed within a relatively small dataset and simulation settings. It will be valuable for future research studies to use larger datasets that are more representative of true clinical conditions and datasets that are clinically validated to determine whether ViTs are advantageous to clinical identification of oncology diagnostic and to improve early detection.

Introduction

Lung cancer has a substantial global burden, affecting millions of lives every year. Due to the cancer's aggressive nature and frequent late-presenting disease state, the mortality associated with lung cancer is high. Early detection is essential because the intervention can improve treatment effectiveness and survival rates a great deal¹. The conventional approach for preliminary screening and diagnosis remains with the utilization of computed tomography (CT) to allow more detailed visualization of the pulmonary structures. However, the evaluation of the CT images is complex and is entirely dependent on the reviewers as radiologists. Tumor var....

Protocol

The dataset includes a total of 1,190 CT scan slices from 110 cases, categorized into three classes: normal (55 cases), benign (15 cases), and malignant (40 cases). Each case comprises multiple CT slices (ranging from approximately 80 to 200 slices per case), offering diverse axial views of the thoracic region. The CT images were obtained in DICOM using the SOMATOM scanner with standard imaging parameters: tube voltage = 120 kV, slice thickness = 1 mm, window width = 350–1,200 Hounsfield Units (HU), and window center values = 50–600 HU, during a full inspiration breath hold.

All images were fully de-identified prior to analysis to remove an....

Results

The Vision Transformer model demonstrated exceptional results on a multiple evaluation metrics on the IQ-OTH/NCCD lung cancer datasets, effectively differentiating normal, benign, and malignant CT images. Overall model performance reached a high accuracy of 98.18% across the test dataset comprised of 220 images. This extremely high accuracy indicates that the model is able to generalize very effectively and can therefore be used in a clinical setting.

Discussion

It has achieved remarkable results in the evaluation and implementation of the Vision Transformer model on the IQ-OTH/NCCD lung cancer dataset with high accuracy. The general accuracy of classifying a CT scan as normal, benign, and malignant is 98.18% using this model.

This superior accuracy is further proven by the model's performance in the other key scores, such as 100% precision for malignant case detection, which is very important in early diagnosis and management.

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
A100 GPU (CUDA)	NVIDIA	CUDA Version 11.6	GPU acceleration for model training and evaluation.
AMD EPYC-7502P CPU	AMD	N/A	Processor used for high-performance computing.
Gigabit Ethernet	Intel	N/A	Networking for peer-to-peer secure communication in CPS.
Matplotlib	Python Software Foundation	Version 3.5	Visualization library for plotting results.
Paillier Cryptosystem	Open Source (implemented via TenSEAL)	N/A	Enables additive homomorphic encryption on gradients.
PySyft	OpenMined	Version 0.6.0	Differential privacy and federated learning library.
Python (Anaconda Distribution)	Anaconda Inc	Version 3.9	Includes pre-installed packages and environment management tools, Used for scripting and framework development.
PyTorch	Meta AI	Version 1.12	Deep learning framework for training models.
RAM	Corsair	256 GigaByte (GB)	High memory support for intensive training.
Scikit-learn	Python Software Foundation	Version 1.1	Machine learning tools for performance evaluation.
Seaborn	Python Software Foundation	Version 0.11	Statistical data visualization library.
SSD Storage	Seagate	1 TeraByte (TB)	For fast data storage and retrieval.
TenSEAL	OpenMined	Version 0.3	Homomorphic encryption library for secure aggregation.
TensorFlow	Google	Version 2.9	Deep learning framework for diffusion models.
Ubuntu OS	Canonical	Version 20.04 LTS	Operating system used for all experiments.