Scale-Invariant Feature Extraction for Skin Image Detection - Adel Al-Zebari, Shahab Wahab Kareem, Sami Hussein Ismael (International Journal of Intelligent Systems and Applications in Engineering, 2024)
In many applications, including dermatology, biometrics, and medical diagnostics, skin image detection is essential. Because of the differences in lighting, positions, and scales, it is difficult to identify skin regions in images. The article presents a new method for scale-invariant feature extraction-based skin image detection. The proposed strategy makes use of scale-invariant features to improve the skin image detection’s resilience at various scales. Scale-invariant feature transform (SIFT) is used to extract key points from skin images, enabling the identification of unique patterns regardless of their size. The skin portions in the image are then reliably represented by using these key points. The incorporation of machine learning methods to improve the skin image recognition procedure is also explored in this research. A model is trained on a broad dataset of skin photos to enable the system to learn and adapt to different skin kinds, circumstances, and image scales. The evaluation’s findings show how well the suggested scale-invariant feature extraction technique works to recognize skin images with reliability and accuracy.