Research on Image Detection and Classification of Colon Cancer Based on Convolutional Neural Networks

This study focuses on colon cancer pathological image detection tasks and systematically compares the classification performance of AlexNet, VGG16, ResNet series, and quantum convolution models. Using public datasets such as LC25000 and CRC5000, along with multi-level experimental design, it explores the application value of deep learning in automated medical image analysis and discusses its clinical translation potential and future research directions.

Colorectal cancer is a malignant tumor with one of the highest incidence and mortality rates globally. Early detection and diagnosis are crucial for improving patient survival rates. Traditional pathological diagnosis relies on manual microscopic interpretation, which is time-consuming, labor-intensive, and prone to subjective factors. The advantages of deep learning technology (especially Convolutional Neural Networks, CNN) in image feature extraction and classification provide a new path for automated pathological image analysis, and this study is conducted based on this background.

The study uses public datasets LC25000 (including lung cancer and colon cancer pathological images) and CRC5000 (fine-grained classification of colorectal cancer). The experimental design includes: single dataset training (training independently with LC25000 or CRC5000), cross-dataset validation (training on LC25000 and testing on CRC5000), and binary classification tasks (simplified classification of normal tissue vs. cancer tissue) to evaluate the model's performance under different data distributions.

Classic CNN Architectures:

• AlexNet: Uses ReLU activation function and Dropout regularization; its architecture is simple but has reference value;
• VGG16: Known for its 3×3 convolution kernel stacking design; increased depth enhances feature extraction ability but has a large number of parameters;
• ResNet series (18/34/50/101/152): Introduces residual connections to solve the gradient vanishing problem in deep networks; can capture subtle lesion features in pathological images and performs excellently in medical imaging. Quantum Convolution Model: QuanvolutionModel combines quantum computing and classical deep learning, using quantum circuits to extract features. Although its application is in the early stage, it provides new ideas for future research.

Training Strategies:

• Optimizers: Compare SGD (learning rate from 1e-4 to 1e-2, momentum 0.9, weight decay 5e-4) and Adam (learning rate 1e-2, beta parameters 0.9/0.999, weight decay 1e-4);
• Learning rate scheduling: Uses StepLR strategy (step size of 5 epochs, decay coefficient gamma=0.1);
• Hardware: Uses Google Colab GPU for accelerated training. Code Structure: Modular design, including data loading (processing LC25000/CRC5000), model definition (various CNNs and quantum models), training scripts, and evaluation tools (calculating accuracy, recall, F1 score, etc.).

Clinical Translation Value: Automated pathological analysis systems can assist doctors in improving work efficiency, reducing missed diagnoses and misdiagnoses, especially in areas with scarce medical resources to make up for the shortage of professional pathologists. Future Research Directions: Introduce attention mechanisms to enhance focus on key areas; explore multi-scale feature fusion to improve lesion detection capabilities; conduct multi-modal analysis combined with clinical metadata; develop lightweight models adapted to edge computing devices to achieve real-time diagnosis.