Master Assignment
[B] Understanding the Effect of Image Quality and Resolution on Skin Lesion Recognition
Type: Master EE/CS/HMI
Period: TBD
Student: (Unassigned)
If you are interested, please contact :
Project Background:
Accurate recognition of skin lesions is crucial for early diagnosis and treatment of skin conditions, including skin cancer. However, the quality and resolution of medical images can significantly impact the performance of machine learning models used for automated diagnosis. This project seeks to investigate how variations in image quality and resolution affect the recognition of skin lesions, with the goal of optimizing diagnostic systems for real-world applications.
Project Overview:
- Study the effect of image quality and resolution on the performance of skin lesion recognition models.
- Develop machine learning models that can handle low-quality images and maintain high recognition accuracy.
- Explore strategies to enhance image quality and improve model robustness across diverse datasets.
- Contribute to the development of reliable, real-world applications in medical imaging for early detection and diagnosis of skin conditions.
Why Join?
- Work on a cutting-edge project applying AI to solve real-world challenges in medical imaging and dermatology.
- Gain practical experience in handling medical datasets, improving model performance for skin lesion recognition.
- Collaborate with experts in AI and medical applications, contributing to research that can save lives by improving diagnostic tools.
Who Should Apply?
- Students with knowledge in machine learning and computer vision.
- Individuals eager to explore the intersection of image analysis and healthcare, contributing to more accurate and accessible diagnostic systems.