Local Chapter Nairobi, Kenya Chapter
Coordinated by Kenya ,
Status: Completed
Project Duration: 04 Sep 2023 - 16 Oct 2023
Open Source resources available from this project
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.
Project plan.
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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.
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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.
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Week 5
Refine the machine learning model based on feedback and testing results. Perform rigorous testing and bug fixes for the application.
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Week 6
Optimize the user experience and ensure seamless functionality. Prepare documentation for deployment and usage of the application. Finalize the project
Learning outcomes.
1. Computer Vision Skills: Gain expertise in image processing, feature extraction, and model training for skin disease detection.
2. Machine Learning Application: Understand the application of machine learning algorithms for real-world healthcare challenges.
3. UI/UX Design: Learn user interface and user experience design principles while creating an intuitive application.
4. Interdisciplinary Collaboration: Develop skills in interdisciplinary teamwork and communication between technologists and healthcare professionals.
