SkinLesionAI

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Description

This repository refers to a final project of Computer Engineering major which contains CNN notebooks using the HAM10000 dataset, a dataset with 7 skin cancer classes:

• Basal cell carcinoma;
• Benign keratosis;
• Bowens disease;
• Dermatofibroma;
• Melanocytic nevi;
• Melanoma;
• Vascular lesion.

Objective

Study the impact of configurations and techniques using vision transformer (accuracy and transfer learning only) and CNN models:

• Architectures:
  • ConvNeXt;
  • ResNetV2;
  • ViT_B/32;
  • Xception.

22.4 GB of model data were generated.

• Mainly techniques used:

  • Data augmentation;
    • Image transformations;
    • Generative Adversarial Networks.
  • Segmentation;
  • Transfer learning.

• Metrics extracted:

  • Accuracy;
  • Loss;
  • Sensibility (Recall);
  • Specificity;
  • F1-score;
  • AUC;
  • Precision;
  • Confusion Matrices.
    • Multiclass;
    • Per class.

Utilization of Different Models and Convolutional Neural Network Techniques to Skin Lesion Classification.pdf