Skin cancer classification

This project focuses on classifying skin cancer lesions using the HAM10000 dataset. The goal is to develop a robust deep learning model capable of identifying seven different classes of skin cancer with high accuracy, leveraging image data and metadata provided in the dataset.

Dataset Information

• Source of dataset
• Data description

Classes of Skin Cancer Lesions:

1.	Melanocytic Nevi (nv)
2.	Melanoma (mel)
3.	Benign Keratosis-like Lesions (bkl)
4.	Basal Cell Carcinoma (bcc)
5.	Actinic Keratoses (akiec)
6.	Vascular Lesions (vas)
7.	Dermatofibroma (df)

Observations

Overview of dataset

Params of model

Output of train data

Train validation and accuracy

Run Locally

Clone the project

  git clone https://link-to-project

Download dataset to ./data directory of project

Install dependencies

  pip install

Run model

  python main.py