DermaEvolve Dataset

Overview

The DermaEvolve dataset is a comprehensive collection of skin lesion images, sourced from publicly available datasets and extended with additional rare diseases. This dataset aims to aid in the development and evaluation of machine learning models for dermatological diagnosis.

Sources

The dataset is primarily derived from:

• HAM10000 (Kaggle link) – A collection of dermatoscopic images with various skin lesion types.
• ISIC Archive (Kaggle link) – A dataset of skin cancer images categorized into multiple classes.
• Dermnet NZ – Used to source additional rare diseases for dataset extension. https://dermnetnz.org/
• Google Database - Images

Categories

The dataset includes images of the following skin conditions:

Common Categories:

• Basal Cell Carcinoma
• Squamous Cell Carcinoma
• Melanoma
• Actinic Keratosis
• Pigmented Benign Keratosis
• Seborrheic Keratosis
• Vascular Lesion
• Melanocytic Nevus
• Dermatofibroma

Rare Diseases (Extended):

To enhance diversity, the following rare skin conditions were added from Dermnet NZ:

• Elastosis Perforans Serpiginosa
• Lentigo Maligna
• Nevus Sebaceus
• Blue Naevus
  

Dataset Characteristics

• Augmented: The dataset consists of augmented images. [1:5 images generated]
• Image Size: 64 x 64 ( for memory restrictions in kaggle)
  The resizing and augmentation are made on dataset from my previously uploaded raw dataset :
  https://www.kaggle.com/datasets/lokeshbhaskarnr/dermaevolve-original-unprocessed/data

Acknowledgements

Special thanks to the authors of the original datasets:

• HAM10000 – Tschandl P, Rosendahl C, Kittler H. The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions.
• ISIC Archive – International Skin Imaging Collaboration (ISIC), a repository for dermatology imaging.
• Dermnet NZ – A valuable resource for dermatological images.

Usage

This dataset can be used for:

• Training deep learning models for skin lesion classification.
• Research on dermatological image analysis.
• Development of computer-aided diagnostic tools.
  Please cite the original datasets if you use this resource in your work.

NOTE :

Check out the github repository for the streamlit application that focuses on skin disease prediction -->
https://github.com/LokeshBhaskarNR/DermaEvolve---An-Advanced-Skin-Disease-Predictor.git
Streamlit Application Link : https://dermaevolve.streamlit.app/
Kindly check out my notebooks for the processed models and code -->

• SMOTE oversampled data link : https://www.kaggle.com/datasets/lokeshbhaskarnr/dermaevolve-augmented-unprocessed-64/data
• CODE - SMOTE oversampling Notebook : https://www.kaggle.com/code/lokeshbhaskarnr/smote-oversampling
• Original Dataset : https://www.kaggle.com/datasets/lokeshbhaskarnr/dermaevolve-original-unprocessed
  Check out my NoteBooks on multiple models trained on this dataset :
• NASNet model : http://kaggle.com/code/lokeshbhaskarnr/skin-disease-pred-dermaevolve-nasnet-mobile
• MobileNet model : https://www.kaggle.com/code/lokeshbhaskarnr/skin-disease-pred-dermaevolve-mobilenet
• Custom CNN model : https://www.kaggle.com/code/lokeshbhaskarnr/dermaevolve-custom-cnn-model-train
• DenseNet 169 model : https://www.kaggle.com/code/lokeshbhaskarnr/skin-disease-pred-dermaevolve-densenet-169