Automatic Skin Cancer Detection System Combining Hybrid Features and Machine Learning

The automatic melanoma detection project based on hybrid feature extraction and machine learning algorithms aims to realize auxiliary identification of skin cancer through image preprocessing, multi-dimensional feature analysis, and classification models. This system can assist in early screening and reduce medical pressure, but it should be noted that it is only for educational research and cannot replace professional diagnosis. The core process of the project includes image preprocessing (denoising, hair removal, etc.), lesion area segmentation, hybrid feature extraction (color, texture, shape, etc.), and machine learning classification, demonstrating the application potential of AI in the medical field.

Skin cancer is one of the most common cancers worldwide. Although melanoma accounts for about 1% of skin cancer cases, it causes most deaths, so early detection is crucial. Traditional diagnosis relies on doctors' experience, which is challenging in areas with insufficient medical resources. Computer vision and machine learning technologies have promoted the development of automated detection systems, which can quickly and objectively evaluate cases, assist in identifying suspected cases, and improve diagnostic efficiency.

The project constructs a complete detection process, with core components including image preprocessing (denoising, hair removal, hole filling), image segmentation (threshold segmentation + morphological processing), feature extraction (hybrid strategy: color features such as RGB channel statistics, texture features such as GLCM and LBP, shape and boundary features such as asymmetry), and machine learning classifiers (supports multiple algorithms, selects the optimal one through cross-validation). Preprocessing includes median filtering and morphological operations; segmentation is done via threshold binarization and multi-step morphological processing; features cover multiple dimensions to enhance discrimination ability.

Model evaluation uses metrics such as accuracy, sensitivity, specificity, and confusion matrix; it also evaluates segmentation quality (pixel-level accuracy compared with manual annotations). System deployment supports local operation, requiring Windows 10+, Intel i5+, 8GB RAM+, and 500MB of space. It provides a graphical interface to upload images, automatically completes the process, and outputs results and confidence levels.

The project is only for educational research and cannot replace professional diagnosis. Current limitations: the diversity of training datasets affects generalization; lighting and shooting angles affect accuracy; there are insufficient samples of rare lesions. Any skin abnormalities should be evaluated by a dermatologist.

Project insights: modular architecture facilitates debugging and optimization, hybrid features improve robustness, and a comprehensive evaluation system values intermediate links. Future directions: introduce deep learning models such as CNN; expand detection to more skin lesions; develop mobile applications; establish larger-scale and diverse datasets.

This system represents an important exploration of medical AI. Although it has limitations, it demonstrates the feasibility and potential of AI-assisted diagnosis. With algorithm optimization and dataset expansion, it is expected to become a reliable auxiliary tool for dermatologists, helping with early detection and treatment.