Intelligent Detection System for Skin Diseases Based on Convolutional Neural Networks

This project is an AI application that uses convolutional neural networks (CNN) for skin disease image classification. It aims to realize automatic recognition and classification of skin lesion images through deep learning technology, providing technical support for early skin disease screening. The project addresses the shortage of dermatologist resources and can be used as an auxiliary tool in primary care, personal health screening, and medical education. However, it should be noted that it cannot replace professional doctors' diagnosis, and its performance is limited by the distribution of training data.

Skin diseases are common health issues worldwide, ranging from ordinary eczema to severe skin cancer. Timely diagnosis is crucial for treatment. However, in many regions, the number of dermatologists is insufficient, leading to long waiting times for patients. AI-based skin disease detection systems identify visual features of lesions through deep learning models, which can provide preliminary screening and auxiliary diagnosis when doctor resources are insufficient, helping patients detect problems early.

The core algorithm of the project is the convolutional neural network (CNN). Its hierarchical feature extraction mechanism can automatically learn multi-layer representations from low-level textures to high-level lesion patterns (convolutional layers extract local features, pooling layers reduce dimensionality and enhance translation invariance). The model is trained on an annotated dataset containing various skin diseases and needs to handle class imbalance issues (using strategies such as data augmentation and class weight adjustment). The project provides training scripts and inference interfaces, allowing users to upload images to obtain prediction results and confidence levels.

The system supports users to upload skin lesion images and automatically performs preprocessing such as size adjustment and normalization. Based on the trained CNN model, it completes image analysis in seconds and outputs lesion type predictions. Results are displayed in an intuitive way, including lesion type names and model confidence levels, helping users understand the basis for judgment.

Data Quality and Diversity: Skin images are affected by shooting equipment, lighting, etc. Model robustness is improved through data augmentation (random rotation, scaling, brightness adjustment). Class Imbalance: Class imbalance is common in medical datasets, which is solved by oversampling minority classes or weighting loss functions. Model Interpretability: Grad-CAM technology is used to visualize the regions the model focuses on, enhancing decision transparency.

Primary Care Assistance: In remote areas, it helps primary care doctors identify cases that need referral, improving diagnosis efficiency. Personal Health Screening: Provides ordinary users with a convenient self-screening method to detect suspicious lesions in time. Medical Education: Assists dermatology residents in learning the visual features of diseases, accelerating experience accumulation.

Limitations: The AI system is only an auxiliary tool and cannot replace professional doctors' diagnosis (needs to be combined with medical history and examinations). The model's performance is limited by the distribution of training data, and its accuracy may decrease for diseases or populations not fully covered. Future Trends: Multimodal fusion (combining text and images), federated learning (collaborative training under privacy protection), edge deployment (offline diagnosis on mobile devices), and continuous learning (updating knowledge from new data).