Detection of Lung Cancer Using Histopathology Based on CNN

Abstract

Early diagnosis of lung cancer is a challenge, making it difficult to treat successfully. This paper discusses the creation of a Convolutional Neural Network (CNN)-based system for lung cancer prediction from Biopsy Scan. Deep learning holds great promise for medical image processing, and CNNs are particularly good at learning complex patterns. Our CNN system will seek to scan Biopsy scans and detect insidious differences consistent with early-stage lung cancer. It can potentially: Early detection of lung cancer can greatly enhance the chances of effective intervention. Automation of image analysis using CNNs saves medical professionals\' time for diagnosis and treatment planning. CNNs show high accuracy in image recognition, which results in more accurate predictions. It helps advance personalized medicine by facilitating earlier and more accurate diagnoses. In addition, it aids in targeted screening programs and improved allocation of resources, hence favorably influencing population health policies. We assess our CNN system\'s functionality and consider its potential to revolutionize lung cancer diagnosis and treatment. By incorporating this technology into practice, we hope to enhance patients\' outcomes as well as ensure an improved responsive and effective health system.

Introduction

Lung Cancer Background
Lung cancer is a leading global cause of cancer-related deaths, with 1.8 million deaths reported in 2020. Early detection is vital for improved treatment and survival rates. One common diagnostic method is a biopsy scan, which uses X-rays and computer imaging to produce detailed 3D cross-sectional views of the lungs and surrounding areas. A PET-biopsy scan combines this method with positron emission tomography to detect metabolic activity, helping to diagnose, stage, and monitor lung cancer.

Role of CNN in Detection
The project uses Convolutional Neural Networks (CNNs) to automate the diagnosis of lung cancer from biopsy scan images. CNNs are highly effective at extracting image features and identifying patterns indicative of cancer. The CNN model in this study classifies images into cancerous or non-cancerous, achieving a high accuracy of 98.6%. The architecture includes:

• Convolutional and max-pooling layers for feature extraction

• Dense (fully connected) layers for prediction

• Softmax output layer for classification probabilities

• Dropout layers to prevent overfitting

Project Contributions

• Development of an AI-driven diagnostic system using CNN

• Web-based application allowing real-time image upload and diagnosis

• Reliable, scalable tool for clinical use

• Reduces dependency on radiologists and minimizes diagnostic errors

Methodology Overview

1. Data Acquisition & Preprocessing: Histopathological biopsy images are resized, normalized, and augmented to increase data diversity.

2. CNN Model Design: Includes convolution, pooling, dropout, and dense layers.

3. Training & Validation: Model trained using labeled data, optimizing with Adam and categorical crossentropy. Achieved 98.6% test accuracy.

4. Model Evaluation: Metrics like precision, recall, F1-score, and confusion matrix confirm robust performance with minimal misclassifications.

5. Deployment: Model integrated into a Flask web app where users upload images and receive instant results.

6. Testing: Extensive functional, integration, and system testing ensures usability and reliability.

Classification Process

• Binary classification: cancerous vs. non-cancerous

• CNN extracts features from images and classifies them based on learned patterns

• Softmax function gives probability-based output

• Metrics like accuracy, precision, recall, and F1-score measure model performance

• Low false positive/negative rates make it suitable for medical applications

Related Work & Literature Review
Prior studies have explored machine learning approaches such as SVM, decision trees, random forest, and k-NN for lung cancer detection. This project builds upon those methods by implementing a deep learning model with significantly higher accuracy and automated end-to-end classification.

Conclusion

In conclusion, this project has been able to convincingly prove the capabilities of Convolutional Neural Networks (CNNs) in the early and accurate identification of lung cancer through the evaluation of biopsy scan images. Through the use of deep learning methods, the constructed system can automatically identify intricate features from histopathological images and classify them with high accuracy into cancerous or non-cancerous categories. The model had a remarkable accuracy of 98.6%, with robust performance in other metrics like precision, recall, and F1-score. Additionally, incorporation of the trained model into a user-friendly web application via Flask improves its usability and real-time accessibility in clinical settings, providing a useful tool for assisting medical professionals in diagnostic decision-making. This project not only overcomes the challenges of manual diagnosis, including time loss and expert interpretation, but also adds to the emerging body of AI in medical imaging. The findings support the validity and feasibility of the suggested system, and with increased development and bigger data sets, it has the potential for real-world application and extended use in healthcare.

References

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Copyright

Copyright © 2025 P. Thirumalesh, V. V. Sai Dinesh, C. Vardhan Kumar, A Rahul, Dr. Munipraveena Rela, Prof. M. E. Palanivel. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.