Academic Journal
Boosting skin cancer diagnosis accuracy with ensemble approach
العنوان: | Boosting skin cancer diagnosis accuracy with ensemble approach |
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المؤلفون: | Priya Natha, Sivarama Prasad Tera, Ravikumar Chinthaginjala, Safia Obaidur Rab, C. Venkata Narasimhulu, Tae Hoon Kim |
المصدر: | Scientific Reports, Vol 15, Iss 1, Pp 1-25 (2025) |
بيانات النشر: | Nature Portfolio, 2025. |
سنة النشر: | 2025 |
المجموعة: | LCC:Medicine LCC:Science |
مصطلحات موضوعية: | Multi-class skin cancer classification, ISIC 2018 dataset, HAM10000 dataset, Machine learning (ML), Random forest (RF), Multi-layer perceptron neural network (MLPN), Medicine, Science |
الوصف: | Abstract Skin cancer is common and deadly, hence a correct diagnosis at an early age is essential. Effective therapy depends on precise classification of the several skin cancer forms, each with special traits. Because dermoscopy and other sophisticated imaging methods produce detailed lesion images, early detection has been enhanced. It’s still difficult to analyze the images to differentiate benign from malignant tumors, though. Better predictive modeling methods are needed since the diagnostic procedures used now frequently produce inaccurate and inconsistent results. In dermatology, Machine learning (ML) models are becoming essential for the automatic detection and classification of skin cancer lesions from image data. With the ensemble model, which mix several ML approaches to take use of their advantages and lessen their disadvantages, this work seeks to improve skin cancer predictions. We introduce a new method, the Max Voting method, for optimization of skin cancer classification. On the HAM10000 and ISIC 2018 datasets, we trained and assessed three distinct ML models: Random Forest (RF), Multi-layer Perceptron Neural Network (MLPN), and Support Vector Machine (SVM). Overall performance was increased by the combined predictions made with the Max Voting technique. Moreover, feature vectors that were optimally produced from image data by a Genetic Algorithm (GA) were given to the ML models. We demonstrate that the Max Voting method greatly improves predictive performance, reaching an accuracy of 94.70% and producing the best results for F1-measure, recall, and precision. The most dependable and robust approach turned out to be Max Voting, which combines the benefits of numerous pre-trained ML models to provide a new and efficient method for classifying skin cancer lesions. |
نوع الوثيقة: | article |
وصف الملف: | electronic resource |
اللغة: | English |
تدمد: | 2045-2322 55991386 |
Relation: | https://doaj.org/toc/2045-2322 |
DOI: | 10.1038/s41598-024-84864-5 |
URL الوصول: | https://doaj.org/article/0a559913862c43b8a8a9995388a1262f |
رقم الانضمام: | edsdoj.0a559913862c43b8a8a9995388a1262f |
قاعدة البيانات: | Directory of Open Access Journals |
تدمد: | 20452322 55991386 |
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DOI: | 10.1038/s41598-024-84864-5 |