Transforming Dermatological Diagnosis: Deep Learning Approaches for Skin Disease Detection in the Digital Era
Yazarlar (7)
Ankit Yadav
Sharda University, Hindistan
Sharda University, Hindistan
Vinay Sharma
Sharda University, Hindistan
Sharda University, Hindistan
Gaurav Raj
Sharda University, Hindistan
Sharda University, Hindistan
Tanupriya Choudhury
Graphic Era Deemed To Be University, Hindistan
Graphic Era Deemed To Be University, Hindistan
Ayan Sar
University Of Petroleum And Energy Studies, Hindistan
University Of Petroleum And Energy Studies, Hindistan
Ketan Kotecha
Symbiosis Institute Of Technology, Hindistan
Symbiosis Institute Of Technology, Hindistan
Prof. Dr. Turgut ÖZSEVEN Tokat Gaziosmanpaşa Üniversitesi, Türkiye
| Bildiri Türü | Tebliğ/Bildiri | Bildiri Dili | İngilizce |
| Bildiri Alt Türü | Tam Metin Olarak Yayınlanan Tebliğ (Uluslararası Kongre/Sempozyum) | ||
| Bildiri Niteliği | Alanında Hakemli Uluslararası Kongre/Sempozyum | ||
| DOI Numarası | 10.1145/3660853.3660924 | ||
| Kongre Adı | Cognitive Models and Artificial Intelligence Conference | ||
| Kongre Tarihi | 25-05-2024 / 26-05-2024 | ||
| Basıldığı Ülke | Türkiye | Basıldığı Şehir | İstanbul |
| Bildiri Linki | http://dx.doi.org/10.1145/3660853.3660924 | ||
| UAK Araştırma Alanları |
Makine Öğrenmesi
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| Özet |
| Recent years have seen a significant increase in interest in dermatological pictures for the detection and identification of skin diseases due to the rising prevalence of skin disorders. This study compares three widely used deep learning models—VGG-16, ResNet50, and Inception V3—for the identification of three common skin conditions: herpes, atopic dermatitis, and acne. Using a dataset of dermatological image data, the study assesses how well each model predicts skin diseases. The performance of each model was assessed based on training duration and computational efficacy. The results show that the VGG-16 and ResNet50 models are computationally efficient, but the Inception V3 model takes longer to train because of its complex architecture. From the performed research, these findings could be utilized as the best algorithm for picture identification in dermatology. Although the comparison analysis of … |
| Anahtar Kelimeler |
| Deep Learning | Inception V3 | ResNet50 | VGG-16 |
BM Sürdürülebilir Kalkınma Amaçları
| Atıf Sayıları | |
| Google Scholar | 1 |