Deep Learning-Based Classification of Skin Lesion Dermoscopic Images for Melanoma Diagnosis
Yazarlar (4)
Humar Kahramanlı Örnek Türkiye
Dr. Öğr. Üyesi Elham Tahsın Yasın YASIN Sinop Üniversitesi, Türkiye
Burak Yılmaz
Doç. Dr. Murat Köklü Selçuk Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SCOPUS dergilerinde yayınlanan tam makale)
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| Dergi Adı | Intelligent Methods in Engineering Sciences | ||
| Dergi ISSN | 2979-9236 | ||
| Dergi Tarandığı Indeksler | Scopus | ||
| Makale Dili | İngilizce | Basım Tarihi | 06-2024 |
| Kabul Tarihi | 30-06-2024 | Yayınlanma Tarihi | 30-06-2024 |
| Cilt / Sayı / Sayfa | 3 / 2 / 70–81 | DOI | 10.58190/imiens.2024.101 |
| Makale Linki | https://doi.org/10.58190/imiens.2024.101 | ||
| UAK Araştırma Alanları |
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| Özet |
| This study examined the effectiveness of artificial intelligence and machine learning methods in accurately categorizing skin cancers. We used the ISIC 2019 Skin Lesion dataset, which consists of images divided into eight categories. To prepare the data, we applied preprocessing techniques and extracted features using the SqueezeNet deep learning model. The dataset was divided into training and test sets using cross validation. Four well-known machine learning algorithms, namely Artificial Neural Network (ANN), k-nearest neighbors (kNN), Random Forest (RF), and Logistic Regression (LR), were employed to perform classification tasks. Each algorithm was specifically designed to suit its processing methodology. The algorithms are assessed based on several important measures. The results indicate that the Artificial Neural Network (ANN) achieved the highest accuracy rate of 71.80%, whereas the k-nearest neighbors (kNN), Random Forest (RF), and Logistic Regression (LR) achieved accuracy rates of 69.70%, 67.00%, and 67.20%, respectively. The results emphasize the capacity of machine learning algorithms to augment clinical decision-making in the field of dermatology, with the goal of enhancing early detection and treatment effectiveness for individuals with skin cancer. |
| Anahtar Kelimeler |
| Deep learning | Lesion Classification | Melanoma | Skin Cancer | Skin Lesion |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Google Scholar | 13 |