ArticleScientific reports2023
A hybrid CNN-Random Forest algorithm for bacterial spore segmentation and classification in TEM images.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.
- Clinical and Imaging Data-based Machine Learning for Early Diagnosis of Bronchopulmonary Dysplasia: A Meta-analysis.Current medical imaging · 2025Pooled it
- Hybrid approach with convolutional neural network and regression models for age estimation in panoramic radiographs.International journal of legal medicine · 2026Observational
- Overcoming resolution constraints in automated colony counting via a high-performance deep learning framework using SAHI.Scientific reports · 2026Article
- Utilizing Artificial Intelligence for CSF Segmentation and Analysis in Head CT Imaging: A Systematic Review.Brain sciences · 2025Review
- Multi-label diagnosis of dental conditions from panoramic x-rays using attention-enhanced deep learning.Oral and maxillofacial surgery · 2025Article
- An intelligent method for detection of small target fungal wheat spores based on an improved YOLOv5 with microscopic images.Plant methods · 2025Article
- Article
- Attention-driven UNet enhancement for accurate segmentation of bacterial spore outgrowth in microscopy images.Scientific reports · 2025Article
- Attention-Enhanced Lightweight Architecture with Hybrid Loss for Colposcopic Image Segmentation.Cancers · 2025Article
- Enhancing automated vehicle identification by integrating YOLO v8 and OCR techniques for high-precision license plate detection and recognition.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present a new approach to segment and classify bacterial spore layers from Transmission Electron Microscopy (TEM) images using a hybrid Convolutional Neural Network (CNN) and Random Forest (RF) classifier algorithm. This approach utilizes deep learning, with the CNN extracting features from images, and the RF classifier using those features for classification. The proposed model achieved 73% accuracy, 64% precision, 46% sensitivity, and 47% F1-score with test data. Compared to other classifiers such as AdaBoost, XGBoost, and SVM, our proposed model demonstrates greater robustness and higher generalization ability for non-linear segmentation. Our model is also able to identify spores with a damaged core as verified using TEMs of chemically exposed spores. Therefore, the proposed method will be valuable for identifying and characterizing spore features in TEM images, reducing labor-intensive work as well as human bias.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.