Skin Cancer Detection Using Machine Learning

Abstract

Skin cancer, a significant global health concern, demands innovative approaches for early detection to improve patient outcomes. This research endeavors to contribute to this imperative by developing an advanced skin cancer detection system using state-of-the-art machine learning (ML) techniques. The study recognizes the challenges in current diagnostic paradigms, which often rely on subjective visual assessments, and aims to create an automated tool that can assist dermatologists in achieving more accurate and timely diagnoses. The research begins by providing a comprehensive overview of the prevalence of skin cancer and its impact on public health. Emphasis is placed on the critical need for early detection to enhance treatment efficacy and reduce mortality rates associated with skin malignancies. The limitations of current diagnostic methods, including the variability in human assessments and the increasing demand for dermatological expertise, underscore the potential of ML as a transformative force in skin cancer diagnosis. To build a robust and generalizable skin cancer detection system, a diverse dataset is curated, comprising images of various skin lesions representing different types of skin cancers and benign conditions. The challenges and considerations in assembling such a dataset are discussed, acknowledging the need for representative samples to avoid biases in the model. Various types of models can be explored, each with its strengths and applications. Here are some common types of machine learning models used in dermatoscopic image analysis Support Convolutional Neural Network (CNN), Vector Machines (SVM), Random Forests, Residual Networks (ResNets), Ensemble Models, Transfer Learning Models. The machine learning architecture chosen for this study is the Convolutional Neural Network (CNN), a powerful deep learning model well-suited for image classification tasks. The rationale behind selecting CNNs is elaborated, emphasizing their ability to automatically learn hierarchical features from images, crucial for distinguishing between malignant and benign lesions.