MELANOMA CLASSIFICATION USING ENHANCED FUZZY CLUSTERING AND DCNN ON DERMOSCOPY IMAGES
Yazarlar (10)
Ganesh Babu Loganathan
Nawroz I. Hamadamen
Dr. Öğr. Üyesi Elham Tahsın Yasın YASIN Sinop Üniversitesi, Türkiye
Amani Tahsin Yasin
Alaa Amer Mohammad
Israa Nabeel Adil
Sidra Bahjat Ismail
Dlanpar Dzhwar Fathullah
Saya Ameer Arsalan
Shaymaa Faruq Hamadameen
| Makale Türü | Özgün Makale (SCOPUS dergilerinde yayınlanan tam makale) | ||
| Dergi Adı | NEUROQUANTOLOGY | ||
| Dergi ISSN | 1303-5150 Dergi Bilgileri (2022) | ||
| Makale Dili | İngilizce | Basım Tarihi | 01-2022 |
| Cilt / Sayı / Sayfa | 20 / 12 / 196–213 | DOI | 10.14704/NQ.2022.20.12.NQ77017 |
| Makale Linki | https://www.academia.edu/download/115454331/NQ77017.pdf | ||
| UAK Araştırma Alanları |
Bilgi Güvenliği ve Kriptoloji
Görüntü İşleme
Yapay Zeka
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| Özet |
| Identifying any type of disease at an earlier stage became an essential thing in the medical field. Cancer especially skin cancer is a major disease that affects humans at a higher rate in the current scenario. Early detection is a significant method to prevent death; treating at an earlier stage leads to the cure of cancer. Researchers proposed a different technique to detect skin cancer. This paper proposed an enhanced DCNN for classifying melanoma (skin cancer) as benign and malignant. The proposed method involves preprocessing, and enhanced fuzzy clustering for detecting melanoma, followed by enhanced DCNN (E-DCNN) for the classification of dermoscopy images. Enhanced fuzzy clustering is a method that combines modified region grow image segmentation along with fuzzy K-means clustering to provide more accurate classified results than other methods proposed by researchers. |
| Anahtar Kelimeler |
| DCNN | Fuzzy K-means clustering | Modified Region-grow segmentation | Dermoscopy | Melanoma | Classification |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Google Scholar | 14 |