Machine Learning Algorithms for Skin Cancer Diagnosis: Comparative Analysis

One of the most serious types of cancer is skin cancer. It is brought on by DNA damage that goes unrepaired in skin cells, which results in genetic flaws or mutations on the skin. Almost three million patients worldwide receive a diagnosis of cancer each year from doctors. It is one of the cancer types that is most well recognized today as being harmful to human health. Thus, early diagnosis is necessary to treat any serious disease in infected people. The research compares algorithms for automatically diagnosing skin cancer based on visual evaluations of skin lesions. In this study uses simple feature descriptors to extract features from images, followed by the use of particular machine learning algorithms for classification. The methods Decision Tree (DT), Random Forest (RF), K-Nearest Neighbors (KNN), Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machine (SVM) were examined and compared. Skin cancer diagnosis using machine learning algorithms has gained significant attention in recent years due to its potential to improve accuracy, efficiency, and accessibility in detecting and classifying skin lesions. It's important to note that the performance of these machine learning algorithms heavily relies on the quality and size of the training data, as well as the preprocessing steps applied to the data. Additionally, ensembles of multiple algorithms or combining different algorithms may further improve diagnostic accuracy. To develop an effective skin cancer diagnostic system, it's crucial to have a diverse and well-labeled dataset of skin lesion images. The dataset should include representative samples of both malignant and benign lesions, covering different skin types, age groups, and lesion characteristics. Moreover, data preprocessing steps, such as image resizing, normalization, and augmentation, can enhance the performance and generalizability of the machine learning models. Ultimately, the integration of machine learning algorithms into clinical practice has the potential to assist dermatologists in making more accurate and efficient skin cancer diagnoses, leading to improved patient outcomes and early intervention.