Skin Disease and Condition Detection using Computer Vision and Machine Learning

Local Chapter Nairobi, Kenya Chapter

Coordinated byKenya ,

Alex Onsando Ombongi

Status: Ongoing

Project background.

Skin diseases and conditions are a significant health concern worldwide, affecting millions of people and posing both medical and social challenges. In Kenya, skin diseases are prevalent due to various factors such as climate, genetics, and lack of awareness. Timely and accurate diagnosis is crucial for effective treatment and management. However, access to dermatological expertise is limited in many regions, leading to delayed diagnosis and inappropriate treatments.

The problem.

The lack of accessible and accurate skin disease detection mechanisms in Kenya results in delayed diagnosis, inadequate treatment, and unnecessary suffering for patients. Traditional diagnosis methods are often error-prone, leading to misdiagnosis and ineffective treatments. The proposed project aims to address this issue by leveraging computer vision and machine learning techniques to develop an AI-powered skin disease detection system.

Project goals.

- Accurate Diagnosis: Develop a computer vision model that can accurately identify common skin diseases and conditions based on images provided by users. - Accessibility: Create a user-friendly mobile or web application that allows individuals to upload images of their skin conditions for rapid assessment. - Education and Awareness: Integrate educational resources within the application to inform users about various skin diseases, their symptoms, and preventive measures. - Data Collection: Collect a diverse dataset of skin disease images representative of the Kenyan population for training and validation purposes. - Community Engagement: Foster collaboration between Omdena local chapters, medical professionals, and technology enthusiasts to address a pressing healthcare issue collectively.

Project plan.

  • Week 1

    Comprehensive literature review on existing skin disease detection methods.
    Research available datasets for skin disease images.
    Define the scope, objectives, and success criteria for the project.
    Set up development environment and tools.

  • Week 2

    Design the architecture for the skin disease detection model.
    Begin developing the machine learning model for skin disease classification.
    Design the user interface and flow of the application.

  • Week 3

    Gather and preprocess data for training and testing the model. Continue developing the machine learning model with iterative improvements. Implement user interface and integrate image uploading functionality.

  • Week 4

    Train and validate the model using the collected dataset. Integrate educational resources about skin diseases within the application. Conduct initial testing and identify areas for improvement.

  • Week 5

    Refine the machine learning model based on feedback and testing results. Perform rigorous testing and bug fixes for the application.

  • Week 6

    Optimize the user experience and ensure seamless functionality. Prepare documentation for deployment and usage of the application. Finalize the project

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