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Related Experiment Videos

Classification of Skin Lesions into Seven Classes Using Transfer Learning with AlexNet.

Khalid M Hosny1, Mohamed A Kassem2, Mohamed M Fouad3

  • 1Department of Information Technology, Faculty of Computers and Informatics, Zagazig, University, Zagazig 44519, Egypt. k_hosny@yahoo.com.

Journal of Digital Imaging
|July 2, 2020
PubMed
Summary

This study introduces an accurate skin lesion classification method using transfer learning with AlexNet. The approach successfully differentiates seven types of skin lesions, aiding early diagnosis of deadly melanoma.

Keywords:
AlexNetClassification of skin lesionsISIC 2018MelanomaTransfer learning

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Area of Science:

  • Dermatology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Accurate skin lesion classification is crucial for early diagnosis and treatment of skin cancer, particularly melanoma.
  • High visual similarity between various skin lesions complicates accurate classification.
  • Early detection of skin cancer significantly improves patient outcomes and survival rates.

Purpose of the Study:

  • To develop and evaluate a highly accurate method for classifying skin lesions into seven distinct categories.
  • To leverage transfer learning with a pre-trained AlexNet model for improved classification performance.
  • To address the challenge of misclassification due to visual similarities among skin lesions.

Main Methods:

  • Utilized transfer learning by employing a pre-trained AlexNet model.
  • Initialized the parameters of the original model and randomly initialized weights of the last three replaced layers.
  • Tested the proposed method on the ISIC 2018 public dataset for skin lesion classification.

Main Results:

  • The proposed method achieved high classification performance across seven skin lesion classes.
  • Achieved an overall accuracy of 98.70%.
  • Demonstrated high sensitivity (95.60%), specificity (99.27%), and precision (95.06%).

Conclusions:

  • The transfer learning approach with AlexNet significantly enhances skin lesion classification accuracy.
  • The method effectively distinguishes between melanoma, melanocytic nevus, basal cell carcinoma, actinic keratosis, benign keratosis, dermatofibroma, and vascular lesions.
  • This accurate classification supports early detection and diagnosis of potentially life-threatening skin cancers.