Detection of Melanoma(a skin cancer) using Convolution Neural Network
The dataset consists of 2357 images of malignant and benign oncological diseases, which were formed from the International Skin Imaging Collaboration (ISIC). All images were sorted according to the classification taken with ISIC, and all subsets were divided into the same number of images, with the exception of melanomas and moles, whose images are slightly dominant.
Link to dataset can be found here - https://drive.google.com/drive/folders/1_LepbKX2nBHmvKYTP8tiHVrTfArH8olu?usp=sharing
The data set contains the following diseases:
- Actinic keratosis
- Basal cell carcinoma
- Dermatofibroma
- Melanoma
- Nevus
- Pigmented benign keratosis
- Seborrheic keratosis
- Squamous cell carcinoma
- Vascular lesion
This will be treated as a multiclass classification problem wherein we will build a custom convolutional neural network in TensorFlow to correctly classify the images into the above types.
The training and test dataset are already separated in the source files mentioned above.