TY  - JOUR
TI  - Deep Learning-Based Classification of Dermoscopic Images for Skin Lesions
AB  - Skin cancer has emerged as a grave health concern leading to significant mortality rates. Diagnosis of this disease traditionally relies on specialist dermatologists who interpret dermoscopy images using the ABCD rule. However, the integration of computer-aided diagnosis technologies is gaining popularity as a means to assist clinicians in accurate skin cancer diagnosis, overcoming potential challenges associated with human error. The objective of this research is to develop a robust system for the detection of skin cancer by employing machine learning algorithms for skin lesion classification and detection. The proposed system utilizes Convolutional Neural Network (CNN), a highly accurate and efficient deep learning technique well-suited for image classification tasks. By using the power of CNN, this system effectively classifies various skin diseases in dermoscopic images associated with skin cancer The MNIST HAM10000 dataset, comprising 10015 images, serves as the foundation for this study. The dataset encompasses seven distinct skin diseases falling within the realm of skin cancer. In this study, diverse transfer learning methods were used and evaluated to enhance the performance of the system. By comparing and analyzing these approaches the highest accuracy rate was obtained using the MobileNetV2 model with a rate of 80.79% accuracy.
AU  - Çakar, Serap
AU  - SELAMET, Feyza
AU  - ÇİT, Gülüzar
AU  - SÖNMEZ, Ahmet Furkan
AU  - Kotan, Muhammed
AU  - Delibasoglu, Ibrahim
DO  - 10.35377/saucis...1314638
PY  - 2023
JO  - Sakarya University Journal of Computer and Information Sciences  (Online)
VL  - 6
IS  - 2
SN  - 2636-8129
SP  - 114
EP  - 122
DB  - TRDizin
UR  - http://search/yayin/detay/1195124
ER  -