Skin Cancer Detection using Machine Learning Algorithms

Skin conditions are among the most common health issues in the world; they are frequently hidden but can bephysically and psychologically painful. Skin cancer may even result from severe cases. One major challenge in medical image analysis is the challenging issue of identifying these disorders from clinical photographs. Expert manual diagnosis takes a lot of time. Computerized skin disease prediction is clearly needed to speed up treatment planning. This study suggests a novel strategy that combines a number of methodsto improve the precision of skin disease detection. We present anew digital hair removal method that makes use of morphological filtering, which includes the inpainting algorithm and the Black-Hat transformation. Gaussian filtering thereafter reduces noise and blur in the images. From skin picture images, the Grey Level Co-occurrence Matrix (GLCM) is utilized to identify important underlying patterns. Using the newly acquired knowledge, certain efficient machine learning algorithms are applied, including K- Nearest Neighbor (KNN) classifiers, Random Forest, and Support Vector Classification (SVC). These classifiers categories skin images into categories including melanocytic nevus (NV), melanoma (MEL) and basal cell carcinoma (BCC) based on the features that have been gathered. Out of all machine learning models Random Forest has more accuracy and classification of images as Random Forest otters Hierarchical Feature Extraction, Data Augmentation, High Performance, Enhanced Data Use, etc. By amalgamating digital image enhancement, segmentation, feature extraction, and machine learning, accurate classification of diverse skin conditions is achieved.