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🦠 ForamDeepSlice: AI-Powered Foraminifera Classification

Automated Fossil Species Identification from Micro-CT Scans Using Deep Learning

KVL Staff on Project

Abdelghafour Halimi
abdelghafour.halimi@kaust.edu.sa
Building 1, Level 0, Office 0125

Collaborators

Didier Barradas-Bautista
didier.barradas@kaust.edu.sa

Ronell Sicat
ronell.sicat@kaust.edu.sa

Ali Alibrahim
ali.alibrahim@kaust.edu.sa

Abdulkader M. Afifi
abdulkader.alafifi@kaust.edu.sa

Research Breakthrough

Foraminifera are microscopic organisms whose fossilized shells reveal Earth's climate history. Our ForamDeepSlice framework uses deep learning to automatically classify 12 foraminifera species from micro-CT scans with unprecedented accuracy.

  • 📊 Tested on 97 specimens representing 27 species, ForamDeepSlice achieved 95.6% accuracy and 99.6% top-3 accuracy across 109,617 2D slices.
  • 🎯 The method includes an interactive dashboard for real-time classification and 3D slice matching.
  • 🔬 This work establishes new benchmarks for AI-assisted micropaleontological identification.

🔬 Research Visualization The workflow diagram below illustrates our comprehensive deep learning pipeline for automated foraminifera classification. The paper entitled “ForamDeepSlice: A High-Accuracy Deep Learning Framework for Foraminifera Species Classification from 2D Micro-CT Slices” is available for download and review here.

🎯 KVL's Contribution <color #000000>KVL's visualization scientists Abdelghafour Halimi, Didier Barradas-Bautista, and Ronell Sicat contributed significantly to the development of the ForamDeepSlice framework through:

  • Design and implementation of PatchEnsemble strategy for improved classification accuracy
  • Curation of comprehensive micro-CT foram dataset with rigorous specimen-level splitting
  • Evaluation and benchmarking of 7 state-of-the-art CNN architectures
  • Development of interactive dashboard with real-time classification and 3D slice matching
  • Optimization of preprocessing pipeline and data augmentation strategies
  • Public release of the ForamDeepSlice framework for scientific community use

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