This GitHub project focuses on the detection and classification of skin cancer through advanced image analysis techniques. Utilizing Scale-Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF) for feature extraction, and the Bag of Visual Words (BoVW) model for image representation, this project aims to provide a robust solution for identifying and classifying skin cancer from dermatological images.
- SIFT Feature Extraction: Extracts distinctive invariant features from images that are used to perform reliable matching between different views of an object or scene.
- SURF Feature Extraction: Utilizes a faster algorithm compared to SIFT while maintaining similar accuracy, facilitating quick feature detection and description.
- Bag of Visual Words Model: Transforms image features into a compact representation, suitable for classification.
- K-Means Clustering: Employs K-Means clustering on extracted features to identify and group similar visual elements, enhancing the classification process.
The primary goal of this project is to leverage these sophisticated image processing and machine learning techniques to accurately detect and classify skin cancer types. By integrating SIFT and SURF for detailed feature extraction, and employing BoVW and K-Means clustering, we aim to develop a system that improves the accuracy and efficiency of skin cancer diagnosis in dermatological practice.