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