Proefschrift
From pixels to patterns: AI-driven image analysis in multiple domains
This thesis investigates the application of deep learning techniques in image analysis across various domains, focusing on four main themes: feature extraction, classification, segmentation, and integration, demonstrating the transformative potential of these technologies.
- Auteur
- S. Javanmardi
- Datum
- 18 september 2024
- Links
- Thesis in Leiden Repository
The research begins by addressing feature extraction and classification challenges in agricultural biotechnology. It introduces efficient deep Convolutional Neural Networks (CNNs) that significantly automate and enhance corn seed classification accuracy, surpassing traditional methods.
The second theme uses advanced CNNs to classify the ripeness stages of mulberries, enhancing sorting accuracy and improving post-harvest processing, which potentially increases economic value and eliminates the need for specialist assessments.
The third theme applies CNNs for segmenting microscope images, particularly focusing on zebrafish larvae in high-throughput settings, demonstrating their ability to accurately differentiate larvae, which supports high-throughput screening and facilitates biological research advancements.
The final theme integrates deep learning with Natural Language Processing (NLP) to refine image captioning techniques, creating more precise, context-aware descriptions beneficial to various fields like biomedical imaging and digital media.
Employing state-of-the-art deep learning models, the thesis tackles distinct, challenging problems, setting new benchmarks and paving the way for future research. This work underscores the efficacy of deep learning in enhancing image analysis and processing, revolutionizing multiple industries and fostering significant societal and technological advancements.