Transactions on Machine Intelligence 2821-1693 1 4 2018 12 01 Skin Cancer Detection from Dermoscopic Images with Emphasis on Shape, Color, and Texture Feature Extraction Using a Two-Stage Classification Algorithm 220 227 220329 10.47176/TMI.2018.220 EN Gh. Shakourian MSc student, Department of Biomedical Engineering, Faculty of Electrical and Computer Engineering, Babol Noshirvani University of Technology, Babol, Iran H. Montazery Kordy Assistant Professor, Department of Biomedical Engineering, Babol Noshirvani University of Technology, Babol, Iran 0000-0002-2010-4945 Journal Article 2018 06 15 Malignant melanoma is one of the most aggressive and life-threatening forms of skin cancer, with a high potential for metastasis if not diagnosed and treated early. The definitive treatment for melanoma is possible when it is accurately detected by a trained specialist in a timely manner. Early detection can lead to a simple excision of the tumor, which can often result in complete cure. However, current diagnostic procedures typically involve a biopsy of the lesion, an invasive and often painful procedure that can cause discomfort to the patient. Given these challenges, this study aims to develop a more efficient, non-invasive method for the early detection of melanoma, leveraging advanced machine learning and image processing techniques. The proposed method utilizes a set of features based on the shape, color, and texture of dermoscopic images, which are extracted to capture critical characteristics of the lesion. These features are then analyzed through a two-stage classification process, designed to categorize the lesion into one of three categories: common nevus, atypical nevus, and melanoma. The method was tested on the PH2 dataset, a well-known dermatological dataset containing images of skin lesions. Results demonstrate that the two-stage classification model achieved an accuracy of approximately 90%, significantly outperforming traditional single-stage classification models for multi-class lesion classification. This innovative approach holds promise for enhancing the accuracy of melanoma detection and reducing the need for invasive procedures, ultimately improving patient outcomes. https://www.tmachineintelligence.ir/article_220329_b98c5413a421a7a962e149f1aff2807d.pdf