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Related Concept Videos

Skin Cancer01:30

Skin Cancer

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Skin cancer is a type of cancer that occurs when there is an abnormal growth of skin cells, usually triggered by damage to the DNA within the skin cells. It is primarily caused by exposure to ultraviolet (UV) radiation from the sun or artificial sources like tanning beds. Skin cancer is the most common type of cancer worldwide, and its incidence continues to rise.
Basal Cell Carcinoma (BCC): BCC is the most common type of skin cancer, accounting for about 80% of cases. It typically develops in...
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Multimodal deep learning ensemble framework for skin cancer detection.

Mayar Ashraf Saeed1, Yasmine M Afify2, Nagwa Lotfy Badr2

  • 1Bioinformatics, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, 11566, Egypt. mayar_ashraf@cis.asu.edu.eg.

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This study enhances skin cancer detection using deep learning and transfer learning. An ensemble model combining pre-trained networks and metadata achieved high accuracy, improving dermatological diagnosis.

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CNNEnsemble learningISIC datasetSMOTESkin lesion classificationTransfer learning

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Area of Science:

  • Dermatology and Medical Imaging
  • Artificial Intelligence in Healthcare
  • Computational Pathology

Background:

  • Skin cancer, a prevalent and potentially fatal condition, necessitates accurate and early detection.
  • Conventional diagnostic methods can be limited, highlighting the need for advanced computational approaches.
  • Deep learning, particularly convolutional neural networks (CNNs), shows promise for automated skin cancer identification and classification.

Purpose of the Study:

  • To develop and evaluate a deep learning model for detecting and classifying multiple types of skin cancer.
  • To assess the efficacy of transfer learning in improving CNN performance for skin cancer diagnosis.
  • To investigate the impact of integrating metadata and ensemble techniques for enhanced diagnostic accuracy.

Main Methods:

  • A convolutional neural network (CNN) model was developed utilizing transfer learning with pre-trained models (ResNet50, Xception, MobileNet, EfficientNetB0, DenseNet121).
  • Metadata integration and an adaptive weighted ensemble method were employed to boost model performance.
  • Synthetic Minority Over-sampling Technique (SMOTE) was used to address class imbalance issues.

Main Results:

  • The proposed ensemble model, fusing ResNet50, Xception, and EfficientNetB0 with metadata, achieved 93.2% accuracy on the ISIC 2018 dataset and 91.1% on ISIC 2019.
  • The model demonstrated strong performance on an external dataset (Derm7pt) with 82.5% accuracy, indicating good generalization.
  • Performance metrics including precision, recall, F1 score, and AUC consistently surpassed existing state-of-the-art methods.

Conclusions:

  • Transfer learning significantly enhances CNN performance for skin cancer detection and classification.
  • The integration of metadata and ensemble techniques provides substantial improvements in diagnostic accuracy.
  • The developed deep learning model offers a promising tool for optimizing dermatological diagnosis and treatment strategies.