Skin Cancer Detention Using Association Classification

Abstract

newline Skin cancer is one of the most prevalent forms of cancer globally, posing significant health challenges and necessitating early and accurate diagnosis for effective treatment. Traditional diagnostic methods often rely on visual inspection and biopsies, which can be time-consuming and subject to human error. Recent advancements in machine learning and image processing techniques have shown promise in enhancing the diagnostic accuracy for skin lesions. This study aims to investigate the efficacy of various classifiers and feature sets in the automated diagnosis of skin cancer. By leveraging state-of-the-art machine learning algorithms, we seek to develop a robust diagnostic framework that can potentially assist dermatologists in making more informed decisions. Through rigorous analysis and validation of different classifiers, this research contributes to the ongoing efforts in improving skin cancer diagnosis, ultimately aiming to enhance patient outcomes and streamline healthcare practices. newlineThis study employs a comprehensive machine learning approach to automate the diagnosis of skin cancer from images of skin lesions. Initially, a dataset consisting of labeled images of skin lesions is collected from reputable medical databases, ensuring a diverse representation of different skin cancer types, including melanoma and non-melanoma. The dataset is pre-processed to enhance image quality and normalize the input dimensions, facilitating effective analysis. Feature extraction techniques, such as Histogram of Oriented Gradients (HOG) and Local Binary Patterns (LBP), are applied to capture relevant visual features from the images. Various machine learning classifiers, including Support Vector Machines (SVM), Random Forests, and Convolutional Neural Networks (CNN), are trained using a subset of the dataset. The models are evaluated using k-fold cross-validation to ensure robust performance assessment and to mitigate overfitting. Performance metrics such as accuracy, precision, recall, and F1 score are computed