This is the Skin Cancer dataset we have imported from kaggle to train our model.
https://www.kaggle.com/tsaideepak/ham10000-data-tree

In addition we have added a 340 images of Healthy skin.
https://drive.google.com/file/d/1QvPj4EEEwqQG7ohO3Ryk4NEquetQeUHI/view?usp=sharing

The dataset contains images of 7 types of Skin Cancers:
1.Actinic keratoses and intraepithelial carcinoma / Bowen's disease (akiec)
2.Basal cell carcinoma (bcc)
3.Benign keratosis-like lesions (solar lentigines / seborrheic keratoses and lichen-planus like keratoses), (bkl)
4.Dermatofibroma (df)
5.Melanoma (mel)
6.Melanocytic nevi (nv)
7.Vascular lesions (angiomas, angiokeratomas, pyogenic granulomas and hemorrhage, vasc)

Arrange the dataset by following the steps given:

1. Create the project file named "SkinCancer" .
2. Create 3 directories named "input", "src" and "outputN".
3. Transfer both the dataset to the input directory and unzip them.
4. Rename the no disease directory as "nod"
5. In the datatree folder created after unzipping the disease dataset, create a dataset named "nod" in the "train", "test" and "validation" folders.
6. Now you are ready to run the code in src.