DermDetect is a skin cancer classification project that utilizes a neural network model trained on a Kaggle dataset and employs MobileNet transfer learning. The project has been deployed using Streamlit, a web application framework for Python.
Skin cancer is a prevalent disease, and early detection plays a crucial role in its successful treatment. DermDetect aims to assist dermatologists and healthcare professionals in accurately classifying skin lesions as benign or malignant. By leveraging deep learning techniques, DermDetect automates the classification process and provides reliable predictions based on input images.
Dataset
Model Architecture
Installation
Usage
The skin cancer dataset used in this project is sourced from Kaggle. It consists of a large collection of skin lesion images categorized into seven classes:
Basal Cell Carcinoma
Melanocytic Nevus
Dermatofibroma
Melanoma
Benign Keratosis
Vascular Lesion
Squamous Cell Carcinoma
This diverse dataset provides a comprehensive representation of various skin conditions, enabling the neural network to learn patterns and make accurate predictions.
DermDetect employs MobileNet as the base model for transfer learning. MobileNet is a lightweight convolutional neural network (CNN) architecture optimized for mobile and embedded vision applications. By leveraging transfer learning, the pre-trained MobileNet model is fine-tuned on the skin cancer dataset, enabling it to learn relevant features and improve classification accuracy.
To run DermDetect locally, follow these steps:
Clone the repository:
bash
Copy code
git clone https://github.com/ImJericho/Skin_Cancer_Classification_Project.git
Navigate to the project directory:
bash
Copy code
cd Skin_Cancer_Classification_Project
Install the required dependencies using pip:
bash
Copy code
pip install -r requirements.txt
Download the pre-trained weights for the MobileNet model from this link and place the model_weights.h5 file in the project directory.
To use DermDetect, execute the following command:
bash
Copy code
streamlit run app.py
This command will start the Streamlit server, and you can access the application by visiting the provided URL in your web browser.
Once the application is launched, you can upload an image of a skin lesion and click the "Classify" button to obtain the predicted class for the lesion. DermDetect will provide you with the probability of the lesion belonging to each of the seven classes mentioned earlier, along with the predicted class label.