Abstract:
This study proposes a convolutional neural network (CNN) model for the classification of lung tissue types, specifically lung adenocarcinoma (lung_aca), lung squamous cell carcinoma (lung_scc), and benign lung tissue (lung_n). The model was trained on a dataset consisting of 15,000 labelled images of lung tissue. The proposed architecture consists of multiple convolutional layers followed by fully connected layers, utilising the sparse categorical cross-entropy loss function and softmax activation for multi-class classification. The model was trained using a data augmentation strategy to improve generalisation and prevent overfitting. The performance of the model was evaluated based on accuracy and loss, showing promising results for the automated classification of lung cancer tissues.
1. Introduction
Lung cancer remains one of the most prevalent and lethal malignancies worldwide, with early and accurate diagnosis playing a critical role in improving survival rates. Histopathological analysis of lung tissue, commonly performed using haematoxylin and eosin (H&E) staining, is a gold-standard method for distinguishing between benign and malignant lung tissues. However, this process is time-consuming and relies on the expertise of pathologists. Recent advances in deep learning, particularly convolutional neural networks (CNNs), have shown significant promise in automating histological image classification, reducing diagnostic workload while maintaining high accuracy.
When examining lung tissue under an H&E stain, the main difference between benign lung tissue, lung adenocarcinoma, and lung squamous cell carcinoma lies in cell arrangement, appearance, and glandular structures:
Benign lung tissue (lung_n) exhibits well-organized, uniform cells with normal architecture.
Lung adenocarcinoma (lung_aca) is characterised by glandular formations with irregular cell borders and nuclei, indicating uncontrolled proliferation of epithelial cells.
Lung squamous cell carcinoma (lung_scc) presents as nests of flat, squamous-like cells with prominent intercellular bridges and keratinisation, typically located more centrally within the airway lumen.
Figure 1. The dataset examples are shown below: (a) lung adenocarcinoma, (b) lung squamous cell carcinoma and (c) lung benign tissue. (Image source: Tian et al., 2024).
Given these distinct histological features, deep learning models can be trained to recognise and classify lung tissue types automatically. In this study, we propose the use of a CNN model for classifying lung tissue types into three categories: lung benign tissue (lung_n), lung adenocarcinoma (lung_aca), and lung squamous cell carcinoma (lung_scc). We utilise a publicly available dataset of 15,000 images of lung tissue and employ image augmentation techniques to improve model generalisation. The CNN architecture was designed to extract high-level features from these images and classify them into three classes.
2. Methodology
2.1. Data Collection and Preprocessing
The dataset used for training and evaluation consists of 15,000 images categorised into three classes: benign lung tissue (lung_n), lung adenocarcinoma (lung_aca), and lung squamous cell carcinoma (lung_scc). The images were extracted from the Kaggle "Lung and Colon Cancer" dataset, which provides labelled histological images of tissue samples. The dataset was organised into three primary directories corresponding to each class.
The preprocessing steps included the following:
Rescaling: All images were rescaled to a range of [0, 1] by dividing pixel values by 255.
Image Augmentation: To enhance the model's ability to generalise, the training images underwent augmentation. This included random rotations, width/height shifts, shear transformations, zooming, and horizontal flips. These transformations were applied to artificially increase the dataset size and prevent overfitting.
2.2. Model Architecture
A deep CNN architecture was employed to classify the lung tissue images. The model consists of the following layers:
Convolutional Layers: The first three layers in the model perform convolution operations with small 3x3 kernels, followed by max-pooling operations with 2x2 windows. The convolutional layers extract features from the images such as edges, textures, and shapes.
Layer 1: 32 filters, kernel size of (3, 3), ReLU activation
Layer 2: 64 filters, kernel size of (3, 3), ReLU activation
Layer 3: 128 filters, kernel size of (3, 3), ReLU activation
Fully Connected Layers: After feature extraction, the flattened output from the convolutional layers is passed through two fully connected layers:
Dense Layer: 128 neurons with ReLU activation
Dropout Layer: A dropout rate of 0.5 was used to reduce overfitting
Output Layer: A softmax activation function was used to output the class probabilities for the three categories.
2.3. Training Procedure
The CNN model was compiled using the Adam optimiser with a learning rate of 0.001. The sparse categorical cross-entropy loss function was used, as the class labels are integers. The model was trained for five epochs, with the dataset split into training and validation sets. A batch size of 32 was used for training, and the training was performed using the GPU available in Google Colab.
2.4. Evaluation Metrics
Model performance was evaluated using the following metrics:
Accuracy: The percentage of correctly classified images out of the total number of images.
Loss: The value of the loss function at the end of each epoch.
These metrics were monitored during the training process, and the model's performance was also evaluated on the validation set.
3. Results
The CNN model achieved the following performance:
Epoch 1:
Training Accuracy: 88.75%
Validation Accuracy: 91.89%
Training Loss: 0.2832
Validation Loss: 0.1992
Epoch 2:
Training Accuracy: 88.61%
Validation Accuracy: 91.89%
Training Loss: 0.2825
Validation Loss: 0.1994
Epoch 3:
Training Accuracy: 90.54%
Validation Accuracy: 94.01%
Training Loss: 0.2407
Validation Loss: 0.1694
Epoch 4:
Training Accuracy: 90.59%
Validation Accuracy: 94.23%
Training Loss: 0.2336
Validation Loss: 0.1624
Epoch 5:
Training Accuracy: 90.95%
Validation Accuracy: 92.19%
Training Loss: 0.2237
Validation Loss: 0.2034
These results demonstrate the model's ability to effectively classify lung tissue images into the three categories with relatively high accuracy. The validation accuracy is close to the training accuracy, suggesting that the model did not overfit to the training data. Additionally, the model's performance showed a steady improvement in both accuracy and loss.
4. Discussion
The results of this study suggest that CNNs are highly effective for the classification of lung cancer tissue types from histological images. The relatively high accuracy achieved in this study indicates that CNNs can automate the process of lung cancer classification, potentially aiding pathologists in diagnosing and distinguishing between different types of lung cancer. This could lead to faster and more accurate diagnoses, improving patient outcomes.
While the model performed well overall, there are areas for potential improvement. For example, the accuracy could be further enhanced by employing more advanced architectures, such as transfer learning with pre-trained models like VGG16, ResNet, or InceptionV3, which have been shown to perform well in image classification tasks. Additionally, further fine-tuning of hyperparameters, such as the learning rate or the number of layers, could lead to better performance.
5. Conclusion
This study demonstrates the successful use of a CNN for classifying lung tissue samples into three categories: benign tissue, lung adenocarcinoma, and lung squamous cell carcinoma. The model achieved high accuracy and shows promise for future use in clinical settings. Further research can be conducted to improve the model’s performance, particularly in addressing class imbalance and exploring more advanced deep learning techniques.
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