20.500.12556/DiRROS-22652 Skin cancer segmentation and classification by implementing a hybrid FrCN-(U-NeT) technique with machine learning Skin cancer is a severe and rapidly advancing condition that can be impacted by multiple factors, including alcohol and tobacco use, allergies, infections, physical activity, exposure to UV light, viral infections, and the effects of climate change. While the steep death tolls continue rising at an alarming rate, lack of symptoms recognition and its preventive measures further worsen the case. In this article, we employ the ISBI-2017 dataset to present an improved FrCN-based hybrid image segmentation method with U-Net to improve detection performance. This paper proposes a hybrid approach using the FrCN-(U-Net) image segmentation technique to enhance results compared to an advanced method for detecting skin cancer types, such as Benign or Melanoma. The classification phase is then handled using the R-CNN algorithm. Our model shows better performance in both training and testing accuracy than any other existing approaches. The results show that the combined method is effective in enhancing early disease diagnosis, which in turn improves treatment outcomes and prognosis. This paper presents an alternative technique for skin cancer detection, which can serve as a guide for clinical practices and public health strategies on how to lower skin-cancer-related deaths. skin tumors cutaneous melanoma lesions imaging techniques cancel detection and diagnosis melanoma preprocessing melignant tumors kožni rak kožni melanom lezije slikovne tehnike odkrivanje in diagnosticiranje raka melanom predobdelava maligni tumorji true false true Angleški jezik Slovenski jezik © 2025 Thapar et al. Neznano 2025-06-15 03:35:32 2025-06-19 13:11:14 2025-10-24 03:51:53 0000-00-00 00:00:00 2025 0 0 Soavtorji: Manik Rakhra, Deepak Prashar, Leo Mrsic, Arfat Ahmad Khan, Seifedine Kadry; Nasl. z nasl. zaslona; Opis vira z dne 14. 6. 2025; str. 1-22 iss. Vol. 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 2025-06-02 004.85:004.92:616-006 1932-6203 10.1371/journal.pone.0322659 239363331 https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0322659 1 438943d6-4978-11f0-bf0a-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=32825 RAZ_Thapar_Puneet_2025.pdf RAZ_Thapar_Puneet_2025.pdf 1 0CB879CA1F20E8E80EE5E045D187A466 ba00acc412ae13db807200e8c1e94421396fa228acd903e3ff9fe085764cf74f 7567fe1f-4ced-11f0-bf0a-001a4af901a5 https://dirros.openscience.si/Dokument.php?lang=slv&id=32986 Rudolfovo – Znanstveno in tehnološko središče Novo mesto 0 0 0