ForamDeepSlice: A High-Accuracy Deep Learning Framework for Foraminifera Species Classification from 2D Micro-CT Slices

• URL: http://arxiv.org/abs/2512.00912v1
• Date: Sun, 30 Nov 2025 14:30:16 GMT
• Title: ForamDeepSlice: A High-Accuracy Deep Learning Framework for Foraminifera Species Classification from 2D Micro-CT Slices
• Authors: Abdelghafour Halimi, Ali Alibrahim, Didier Barradas-Bautista, Ronell Sicat, Abdulkader M. Afifi,
• Abstract summary: This study presents a comprehensive deep learning pipeline for the automated classification of 12 foraminifera species.<n>We curated a scientifically rigorous dataset comprising 97 micro-CT scanned specimens across 27 species.<n>We employed specimen-level data splitting, resulting in 109,617 high-quality 2D slices.
• Score: 0.5219568203653523
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This study presents a comprehensive deep learning pipeline for the automated classification of 12 foraminifera species using 2D micro-CT slices derived from 3D scans. We curated a scientifically rigorous dataset comprising 97 micro-CT scanned specimens across 27 species, selecting 12 species with sufficient representation for robust machine learning. To ensure methodological integrity and prevent data leakage, we employed specimen-level data splitting, resulting in 109,617 high-quality 2D slices (44,103 for training, 14,046 for validation, and 51,468 for testing). We evaluated seven state-of-the-art 2D convolutional neural network (CNN) architectures using transfer learning. Our final ensemble model, combining ConvNeXt-Large and EfficientNetV2-Small, achieved a test accuracy of 95.64%, with a top-3 accuracy of 99.6% and an area under the ROC curve (AUC) of 0.998 across all species. To facilitate practical deployment, we developed an interactive advanced dashboard that supports real-time slice classification and 3D slice matching using advanced similarity metrics, including SSIM, NCC, and the Dice coefficient. This work establishes new benchmarks for AI-assisted micropaleontological identification and provides a fully reproducible framework for foraminifera classification research, bridging the gap between deep learning and applied geosciences.

Related papers

• Transfer Learning-Based CNN Models for Plant Species Identification Using Leaf Venation Patterns [0.0]
  This study evaluates the efficacy of three deep learning architectures: ResNet50, MobileNetV2, and EfficientNetB0 for automated plant species classification based on leaf venation patterns.
  arXiv  Detail & Related papers  (2025-09-03T21:23:09Z)
• Advanced Deep Learning Techniques for Classifying Dental Conditions Using Panoramic X-Ray Images [0.0]
  This study investigates deep learning methods for automated classification of dental conditions in panoramic X-ray images.<n>Three approaches were evaluated: a custom convolutional neural network (CNN), hybrid models combining CNN feature extraction with traditional classifiers, and fine-tuned pre-trained architectures.<n>Results show that hybrid models improve discrimination of morphologically similar conditions and provide efficient, reliable performance.
  arXiv  Detail & Related papers  (2025-08-27T04:52:50Z)
• A Feature-Level Ensemble Model for COVID-19 Identification in CXR Images using Choquet Integral and Differential Evolution Optimization [0.7510165488300369]
  An effective strategy to mitigate the COVID-19 pandemic involves integrating testing to identify infected individuals.<n>While RT-PCR is considered the gold standard for diagnosing COVID-19, it has some limitations such as the risk of false negatives.<n>This paper introduces a novel Deep Learning Diagnosis System that integrates pre-trained Deep Conal Neural Networks (DCNNs) within an ensemble learning framework.
  arXiv  Detail & Related papers  (2025-01-14T16:28:02Z)
• CIRCA: comprehensible online system in support of chest X-rays-based COVID-19 diagnosis [37.41181188499616]
  Deep learning techniques can help in the faster detection of COVID-19 cases and monitoring of disease progression. Five different datasets were used to construct a representative dataset of 23 799 CXRs for model training. A U-Net-based model was developed to identify a clinically relevant region of the CXR.
  arXiv  Detail & Related papers  (2022-10-11T13:30:34Z)
• An Efficient End-to-End Deep Neural Network for Interstitial Lung Disease Recognition and Classification [0.5424799109837065]
  This paper introduces an end-to-end deep convolution neural network (CNN) for classifying ILDs patterns. The proposed model comprises four convolutional layers with different kernel sizes and Rectified Linear Unit (ReLU) activation function. A dataset consisting of 21328 image patches of 128 CT scans with five classes is taken to train and assess the proposed model.
  arXiv  Detail & Related papers  (2022-04-21T06:36:10Z)
• G-DetKD: Towards General Distillation Framework for Object Detectors via Contrastive and Semantic-guided Feature Imitation [49.421099172544196]
  We propose a novel semantic-guided feature imitation technique, which automatically performs soft matching between feature pairs across all pyramid levels. We also introduce contrastive distillation to effectively capture the information encoded in the relationship between different feature regions. Our method consistently outperforms the existing detection KD techniques, and works when (1) components in the framework are used separately and in conjunction.
  arXiv  Detail & Related papers  (2021-08-17T07:44:27Z)
• Deep Implicit Statistical Shape Models for 3D Medical Image Delineation [47.78425002879612]
  3D delineation of anatomical structures is a cardinal goal in medical imaging analysis. Prior to deep learning, statistical shape models that imposed anatomical constraints and produced high quality surfaces were a core technology. We present deep implicit statistical shape models (DISSMs), a new approach to delineation that marries the representation power of CNNs with the robustness of SSMs.
  arXiv  Detail & Related papers  (2021-04-07T01:15:06Z)
• Automated Model Design and Benchmarking of 3D Deep Learning Models for COVID-19 Detection with Chest CT Scans [72.04652116817238]
  We propose a differentiable neural architecture search (DNAS) framework to automatically search for the 3D DL models for 3D chest CT scans classification. We also exploit the Class Activation Mapping (CAM) technique on our models to provide the interpretability of the results.
  arXiv  Detail & Related papers  (2021-01-14T03:45:01Z)
• VoxelHop: Successive Subspace Learning for ALS Disease Classification Using Structural MRI [30.469124322749828]
  We present a subspace learning model, termed VoxelHop, for accurate classification of Amyotrophic Lateral Sclerosis (ALS) Compared with popular convolutional neural network (CNN) architectures, VoxelHop has modular and transparent structures with fewer parameters without any backpropagation. Our framework can easily be generalized to other classification tasks using different imaging modalities.
  arXiv  Detail & Related papers  (2021-01-13T15:25:57Z)
• COVID-MTL: Multitask Learning with Shift3D and Random-weighted Loss for Automated Diagnosis and Severity Assessment of COVID-19 [39.57518533765393]
  There is an urgent need for automated methods to assist accurate and effective assessment of COVID-19. We present an end-to-end multitask learning framework (COVID-MTL) that is capable of automated and simultaneous detection (against both radiology and NAT) and severity assessment of COVID-19.
  arXiv  Detail & Related papers  (2020-12-10T08:30:46Z)
• Classification of COVID-19 in CT Scans using Multi-Source Transfer Learning [91.3755431537592]
  We propose the use of Multi-Source Transfer Learning to improve upon traditional Transfer Learning for the classification of COVID-19 from CT scans. With our multi-source fine-tuning approach, our models outperformed baseline models fine-tuned with ImageNet. Our best performing model was able to achieve an accuracy of 0.893 and a Recall score of 0.897, outperforming its baseline Recall score by 9.3%.
  arXiv  Detail & Related papers  (2020-09-22T11:53:06Z)
• CovidDeep: SARS-CoV-2/COVID-19 Test Based on Wearable Medical Sensors and Efficient Neural Networks [51.589769497681175]
  The novel coronavirus (SARS-CoV-2) has led to a pandemic. The current testing regime based on Reverse Transcription-Polymerase Chain Reaction for SARS-CoV-2 has been unable to keep up with testing demands. We propose a framework called CovidDeep that combines efficient DNNs with commercially available WMSs for pervasive testing of the virus.
  arXiv  Detail & Related papers  (2020-07-20T21:47:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.