Overview / Usage

1. Manual Diagnosis Challenges: Traditional diagnosis of lung cancer through histopathological examination is time-consuming and subjective, leading to variability in diagnoses among pathologists.
2. Accuracy and Efficiency: Automating the classification process using deep learning techniques can improve the accuracy and efficiency of lung cancer diagnosis, enabling faster and more consistent identification of cancer subtypes.
3. Clinical Utility: By providing clinicians with a reliable tool for automated lung cancer classification, this project aims to enhance clinical decision-making and patient care, leading to improved treatment outcomes and prognosis.

The developed deep learning model leverages convolutional neural networks (CNNs), specifically the EfficientNetB2 architecture, pre-trained on ImageNet. By fine-tuning this model on a curated dataset of annotated histopathological images, the project aims to train a robust classifier capable of identifying adenocarcinoma, squamous cell carcinoma, small cell carcinoma, and other lung cancer subtypes with high precision.

In production, the trained model can be integrated into existing healthcare systems or diagnostic tools used by pathologists and oncologists. It can assist clinicians in rapidly and accurately classifying lung cancer types from histopathological images, providing valuable insights to guide treatment planning and patient management. Additionally, the model's performance can be continuously monitored and improved through feedback mechanisms and updates based on real-world usage and new data. Overall, this research contributes to advancing the field of computational pathology and improving the quality of care for patients with lung cancer.

Methodology / Approach

Here's a breakdown of the key components and techniques used:

1. Data Collection and Preprocessing:

   • Histopathological images of lung tissue samples are collected from various sources, ensuring a diverse and representative dataset.
   • The images undergo preprocessing steps such as resizing, normalization, and augmentation to enhance model generalization and robustness.

2. Model Architecture Selection:

   • A Convolutional Neural Network (CNN) architecture is chosen as the backbone for the deep learning model due to its effectiveness in image classification tasks.
   • EfficientNetB2, a state-of-the-art CNN architecture known for its balance between model size and accuracy, is selected as the base model.
   • Transfer learning is employed by initializing the model with weights pretrained on the ImageNet dataset, allowing the model to leverage learned features for improved performance on the lung cancer classification task.

3. Fine-tuning and Training:

   • The pretrained EfficientNetB2 model is fine-tuned on the curated dataset of lung cancer histopathological images.
   • During training, the model's parameters are updated using backpropagation and gradient descent to minimize a chosen loss function, such as categorical cross-entropy.
   • Techniques like learning rate scheduling, early stopping, and model checkpointing are used to optimize training efficiency and prevent overfitting.

4. Evaluation and Validation:

   • The trained model is evaluated using standard metrics such as accuracy, precision, recall, and F1-score on a held-out validation set.
   • Cross-validation or stratified sampling may be employed to ensure robustness and generalization of the model's performance across different subsets of the data.
   • Confusion matrices and ROC curves may also be analyzed to assess the model's performance across different lung cancer subtypes.

5. Deployment and Integration:

   • Once the model achieves satisfactory performance on the validation set, it can be deployed into production environments.
   • Integration into existing healthcare systems or diagnostic tools allows clinicians to access the automated lung cancer classification functionality seamlessly.
   • Model inference can be accelerated using hardware accelerators such as GPUs or TPUs to ensure real-time or near-real-time performance in clinical settings.

6. Continuous Improvement:

   • The model's performance is continuously monitored in production, and updates may be rolled out based on feedback from clinicians or new data.
   • Techniques such as transfer learning from related tasks or domain adaptation may be explored to further improve the model's performance as more data becomes available.

By leveraging these methodologies and technologies, the project aims to develop a robust and efficient solution for automating the classification of lung cancer types, ultimately enhancing clinical decision-making and patient care.

Technologies Used

1.Python

2.Tensorflow

3.One DNN

1. OpenCV

5 NumPy and Pandas

Repository

https://github.com/Vijay18003/Lungcancer_prediction_OneDnn/tree/main