# Image-Based Classical Features and Machine Learning Analysis of Skin Cancer Instances
## 1 Introduction
By analyzing patient electronic health records, genomics data, and medical literature, AI algorithms can identify patterns and correlations that enable the prediction of disease progression and the selection of optimal treatment options for individual patients. For instance, AI models have been developed to predict the response of cancer patients to different treatment regimens, allowing clinicians to tailor therapies to specific patient characteristics [13]. This approach holds the potential to improve treatment outcomes, minimize adverse effects, and optimize therapy effectiveness. The contributions of AI in healthcare are constantly evolving, and ongoing research and advancements continue to push the boundaries of its applications. By leveraging the power of AI, the healthcare industry aims to enhance diagnostic accuracy, optimize treatment strategies, and improve overall patient care. The work in [14] demonstrates the use of a deep neural network for skin cancer classification, achieving performance comparable to dermatologists in distinguishing between malignant melanomas and benign skin lesions. The authors of [15] compare the diagnostic performance of a deep learning convolutional neural network (CNN) with 58 dermatologists in recognizing melanoma based on dermoscopic images. The results demonstrate the potential of deep learning models for accurate melanoma detection. In Reference [16], the accuracy of human readers and machine learning algorithms, including deep learning models, is compared for the classification of pigmented skin lesions. It highlights the potential of machine learning algorithms in improving diagnostic accuracy. Reference [17] proposes a deep learning approach for automated melanoma recognition in dermoscopy images using very deep residual networks. The results demonstrate the effectiveness of deep learning models in achieving accurate detections. Reference [18] describes the ISIC 2017 Skin Lesion Analysis Towards Melanoma Detection Challenge, where deep learning algorithms were applied to classify skin lesions. The study provides insights into the latest developments and challenges in using deep learning for skin. Reference [19] presents a deep transfer learning method for the classification of skin cancer. The study highlights the advantages of leveraging pre-trained models and fine-tuning them for the specific task of skin cancer classification. The proposed approach shows promising results and hasthe potential to contribute to improved diagnostic accuracy in the field of dermatology. In Reference [20], the authors explore the application of transfer learning algorithms to compare different methods of stroke classification in the field of computer science. The paper provides an overview of the experimental setup and methodology used for the comparison. The results and findings of the study are discussed, emphasizing the effectiveness of transfer learning in stroke classification. The aim of this article is to suggest an exhaustive set of experiments that utilize the skin cancer image dataset for the purpose of exploring numerous expressive parameters. By examining the image instances in detail, we conducted a thorough investigation into the extraction of conventional features from the images and subsequently assessed their effectiveness using traditional machine learning models. We also targeted seven diseases evaluations, including Actinic Keratosis/Intraepithelial Carcinoma (AKIE), Basal Cell Carcinoma (BCC), Benign Keratosis-like Lesions (BLK), Dermatofibroma (DF), Melanoma (MEL), Nevus (NV), and Vascular Lesions (VASC). As such, in this article, we employ six feature extraction approaches, which we model using six classical classifiers. We address skin cancer detection as both a seven-class problem and a two-class problem comprising 21 permutations of skin cancer instances. Our experimental results demonstrate that Random Forest achieves the highest performance, and the Edge Histogram (EH) and Fuzzy Opponent Histogram (FoH) feature sets perform best in learning the skin cancer model.