AI Innovation Lab Intern – Data Labeling for Medical Image Diagnosis
Developed and trained a Convolutional Neural Network (CNN) in Python to analyze corneal confocal microscopy images for diagnostic purposes in a healthcare research setting. Conducted data preprocessing, dataset organization, and model evaluation to enhance accuracy and reliability of the neural network. Applied CCMetrics software to quantify corneal nerve fibre degeneration, facilitating data-driven clinical diagnoses and supporting medical research initiatives. • Annotated and labeled medical imaging data (corneal confocal microscopy). • Segregated image data into training, validation, and test sets for model development. • Evaluated model predictions and labeled diagnostic outcomes for disease detection. • Used CCMetrics for statistical analysis and quantification of degeneration.