TY  - JOUR
AU  - Mubarik, Tuba
AU  - Aftab, Shabib
PY  - 2026
DA  - 2026/10/09
TI  - Skin Cancer Detection Using Data Fusion and Deep Transfer Learning Techniques
JO  - ICCK Journal of Image Analysis and Processing
T2  - ICCK Journal of Image Analysis and Processing
JF  - ICCK Journal of Image Analysis and Processing
VL  - 2
IS  - 4
SP  - 218
EP  - 230
DO  - 10.62762/JIAP.2026.243543
UR  - https://www.icck.org/article/abs/JIAP.2026.243543
KW  - Skin cancer
KW  - binary classification
KW  - dataset fusion
KW  - transfer learning
KW  - ResNet50
KW  - DenseNet121
KW  - PAD-UFES-20
KW  - ISIC 2016
AB  - Skin cancer remains a major healthcare challenge. Early detection is important because it increases the chances of successful treatment. Deep learning has become an effective approach for automated skin lesion classification. However, many existing studies rely on a single dataset, which limits the diversity of training images and may reduce model generalization. This study proposes a transfer learning framework for binary skin cancer classification using dataset-level fusion. Two public datasets, PAD-UFES-20 and ISIC~2016, are combined to create a fused dataset that contains both clinical smartphone images and dermoscopic images. The images are resized to 224 × 224 pixels, converted to RGB format, and divided into training, validation, and testing sets using stratified splitting. Data augmentation and class weighting are applied to improve model learning. Two pretrained models, ResNet50 and DenseNet121, are fine-tuned using a two-phase training strategy. Model performance is evaluated using accuracy, sensitivity, specificity, positive and negative predictive values, AUC-ROC, and confusion matrices. Experimental results show that ResNet50 achieved an accuracy of 82.19%, while DenseNet121 achieved the best accuracy of 82.98%. The findings suggest that dataset-level fusion improves training data diversity and provides reliable performance for binary skin cancer classification. The proposed framework is organized into a Training Layer and a Testing Layer, which separate model development from inference. This design provides a flexible framework for future medical image classification applications.
SN  - 3068-6679
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Mubarik2026Skin,
  author = {Tuba Mubarik and Shabib Aftab},
  title = {Skin Cancer Detection Using Data Fusion and Deep Transfer Learning Techniques},
  journal = {ICCK Journal of Image Analysis and Processing},
  year = {2026},
  volume = {2},
  number = {4},
  pages = {218-230},
  doi = {10.62762/JIAP.2026.243543},
  url = {https://www.icck.org/article/abs/JIAP.2026.243543},
  abstract = {Skin cancer remains a major healthcare challenge. Early detection is important because it increases the chances of successful treatment. Deep learning has become an effective approach for automated skin lesion classification. However, many existing studies rely on a single dataset, which limits the diversity of training images and may reduce model generalization. This study proposes a transfer learning framework for binary skin cancer classification using dataset-level fusion. Two public datasets, PAD-UFES-20 and ISIC~2016, are combined to create a fused dataset that contains both clinical smartphone images and dermoscopic images. The images are resized to 224 × 224 pixels, converted to RGB format, and divided into training, validation, and testing sets using stratified splitting. Data augmentation and class weighting are applied to improve model learning. Two pretrained models, ResNet50 and DenseNet121, are fine-tuned using a two-phase training strategy. Model performance is evaluated using accuracy, sensitivity, specificity, positive and negative predictive values, AUC-ROC, and confusion matrices. Experimental results show that ResNet50 achieved an accuracy of 82.19\%, while DenseNet121 achieved the best accuracy of 82.98\%. The findings suggest that dataset-level fusion improves training data diversity and provides reliable performance for binary skin cancer classification. The proposed framework is organized into a Training Layer and a Testing Layer, which separate model development from inference. This design provides a flexible framework for future medical image classification applications.},
  keywords = {Skin cancer, binary classification, dataset fusion, transfer learning, ResNet50, DenseNet121, PAD-UFES-20, ISIC 2016},
  issn = {3068-6679},
  publisher = {Institute of Central Computation and Knowledge}
}