Intelligent Diagnosis System for Lung Cancer CT Images Based on Convolutional Neural Networks: From Model Construction to Clinical Deployment

This article analyzes the LungCancerCNN open-source project, which combines Convolutional Neural Networks (CNN) with the Flask web framework to build an end-to-end medical image diagnosis system. It supports automatic classification of four types—adenocarcinoma, large cell carcinoma, squamous cell carcinoma, and normal tissue—and outputs confidence scores, providing a complete technical reference for the implementation of medical AI. It aims to address the pain points of traditional CT diagnosis, such as reliance on experience, missed or misdiagnosis, and uneven resource distribution.

Lung cancer is the malignant tumor with the highest incidence and mortality rate globally, with approximately 2.2 million new cases and 1.8 million deaths each year. Early diagnosis is crucial (5-year survival rate for stage I is over 90%, while for stage IV it is less than 10%). Traditional CT diagnosis relies on doctors' experience, which is prone to missed or misdiagnosis due to fatigue, and high-quality resources are unevenly distributed—these factors have spurred the development of AI-assisted diagnosis technology for medical images.

LungCancerCNN adopts a layered architecture, divided into four modules: data preprocessing, feature extraction, classification inference, and result display. The core technology stack includes: deep learning frameworks TensorFlow/Keras; web service framework Flask; image processing tools OpenCV/Pillow; data science libraries NumPy/Pandas; and visualization library Matplotlib.

The project targets a four-classification task for lung cancer CT images: 1. Adenocarcinoma (accounting for 40%, originating from alveolar cells); 2. Large cell carcinoma (accounting for 3%, highly invasive); 3. Squamous cell carcinoma (25-30%, related to smoking); 4. Normal tissue (control group). The multi-classification design needs to distinguish between benign/malignant and subtypes to provide detailed information for treatment.

Advantages of CNN: Local perception and weight sharing (reduces parameters, prevents overfitting), hierarchical feature learning (simulates doctors' image reading), and translation invariance (robustness). Network Architecture: Input layer (unified size), convolution block (convolution + batch normalization + ReLU), pooling layer, fully connected layer, Dropout layer, output layer (Softmax). Training Optimization: Data augmentation (geometric transformation, brightness adjustment, noise injection); cross-entropy loss function and Adam optimizer; validation set monitoring + early stopping mechanism to prevent overfitting.

Flask Deployment: Provides RESTful APIs (image upload, prediction, result display); confidence level classification (>85% high confidence, 60-85% needs review, <60% abnormal prompt); front-end includes upload area, preview window, result panel, and history records. Clinical Applications: Assists primary hospitals in screening and referral; improves radiology reading efficiency by over 30%; large-scale screening in physical examination centers.

Current Challenges: Dependence on data annotation quality, generalization ability affected by equipment/parameters, insufficient interpretability. Development Directions: Multi-modal fusion (CT + pathology + genomics), 3D CNN voxel analysis, interpretable AI (Grad-CAM heatmap), federated learning (collaborative training under privacy protection).

LungCancerCNN covers the entire process of data preparation, model training, and deployment, providing a reference for medical AI. It is expected to alleviate resource shortages and improve diagnosis efficiency. However, attention should be paid to ethical issues such as data privacy and algorithmic bias to ensure that technology serves the progress of human health.