Reference health
Automated Bacterial Classifications Using Machine Learning Based Computational Techniques: Architectures, Challenges and Open Research Issues
3 of 55 checkable references need attention · checked 2026-07-25
At the dated check, the references listed below either did not resolve in Crossref or DataCite, or carried a retraction notice. Each one is shown with the registry record that put it there.
14 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.
References needing attention
does not resolve to a known work10.1145/2808719.28087
does not resolve to a known work10.1010/j.infrared.2020.103220
marked retracted by the publisher10.1007/s11063-018-09976-2
RETRACTED ARTICLE: Recent Deep Learning Techniques, Challenges and Its Applications for Medical Healthcare System: A Review
The 52 checked references that resolve
resolves10.32604/iasc.2020.010114
A Perspective of Machine Learning Approach for the Packet Classification in the Software-Defined Network
resolves10.1007/s10462-019-09735-2
Segmentation of cervical cells for automated screening of cervical cancer: a review
resolves10.32604/iasc.2020.010107
Guest Editorial : Special Section on Emerging Challenges in Computational Intelligence for Signal Processing Applications
resolves10.32604/cmc.2020.07988
Multi-Scale Analysis of Fretting Fatigue in Heterogeneous Materials Using Computational Homogenization
resolves10.37398/JSR.2020.640254
Performance Evaluation of Machine Learning Techniques for Mustard Crop Yield Prediction from Soil Analysis
resolves10.1007/s00500-018-3446-9
Recognizing gender from human facial regions using genetic algorithm
resolves10.1007/s10462-020-09804-x
Usage and implementation of neuro-fuzzy systems for classification and prediction in the diagnosis of different types of medical disorders: a decade review
resolves10.1007/s11042-017-4750-6
Text recognition in scene image and video frame using Color Channel selection
resolves10.1007/s10044-019-00827-x
A novel feature descriptor for image retrieval by combining modified color histogram and diagonally symmetric co-occurrence texture pattern
resolves10.32604/cmc.2019.05882
Computational Machine Learning Representation for the Flexoelectricity Effect in Truncated Pyramid Structures
resolves10.2991/ijcis.d.191209.001
An Advanced Deep Residual Dense Network (DRDN) Approach for Image Super-Resolution
resolves10.1016/j.patcog.2017.12.021
Environmental microorganism classification using conditional random fields and deep convolutional neural networks
resolves10.1007/s10462-017-9572-4
A survey for the applications of content-based microscopic image analysis in microorganism classification domains
resolves10.1007/s11831-021-09639-x
Machine Learning and Deep Learning Based Computational Approaches in Automatic Microorganisms Image Recognition: Methodologies, Challenges, and Developments
resolves10.1016/j.bbe.2018.05.007
Automatic detection of tuberculosis bacilli from microscopic sputum smear images using deep learning methods
resolves10.1049/ic:19981039
Image processing and neural computing used in the diagnosis of tuberculosis
resolves10.1007/s002480000004
CMEIAS: A Computer-Aided System for the Image Analysis of Bacterial Morphotypes in Microbial Communities
resolves10.1109/CSSE.2008.485
Application of Support Vector Machine to Heterotrophic Bacteria Colony Recognition
resolves10.1109/ISDA.2010.5687018
A genetic algorithm-neural network approach for Mycobacterium tuberculosis detection in Ziehl-Neelsen stained tissue slide images
resolves10.1109/TITB.2009.2028339
Classification of <i>Mycobacterium tuberculosis</i> in Images of ZN-Stained Sputum Smears
resolves10.1504/IJCBDD.2011.041414
Identification and classification of cocci bacterial cells in digital microscopic images
resolves10.1109/ICEEI.2011.6021502
Automatic classification of tuberculosis bacteria using neural network
resolves10.1109/TITB.2012.2222654
Classification of Bacterial Contamination Using Image Processing and Distributed Computing
resolves10.1007/978-81-322-0740-5_133
Extraction of Bacterial Clusters from Digital Microscopic Images through Statistical and Neural Network Approaches
resolves10.1109/IST.2014.6958454
Multistage classification for bacterial colonies recognition on solid agar images
resolves10.1007/s11760-014-0708-6
Random forest-based tuberculosis bacteria classification in images of ZN-stained sputum smear samples
resolves10.1007/s11694-015-9301-0
Identification of Staphylococcus species with hyperspectral microscope imaging and classification algorithms
resolves10.1016/j.bbe.2016.06.008
Automated object and image level classification of TB images using support vector neural network classifier
resolves10.1109/CHILECON.2017.8229512
Automatic classification of light field smear microscopy patches using Convolutional Neural Networks for identifying mycobacterium tuberculosis
resolves10.1007/978-3-319-68548-9_46
CNN-Based Identification of Hyperspectral Bacterial Signatures for Digital Microbiology
resolves10.1109/CIBEC.2018.8641799
Automated classification of Bacterial Images extracted from Digital Microscope via Bag of Words Model
resolves10.1109/ICECE.2018.8636750
Classification of Microscopic Images of Bacteria Using Deep Convolutional Neural Network
resolves10.1109/ISRITI.2018.8864478
Pathogenic Bacteria Genus Classification using Support Vector Machine
resolves10.1371/journal.pcbi.1006628
Performance of convolutional neural networks for identification of bacteria in 3D microscopy datasets
resolves10.1007/s11760-019-01509-1
Automated identification of mycobacterium bacillus from sputum images for tuberculosis diagnosis
resolves10.1109/ITC-CSCC.2019.8793320
Bacteria Classification using Image Processing and Deep learning
resolves10.1109/ECACE.2019.8679397
Combining Deep Convolutional Neural Network with Support Vector Machine to Classify Microscopic Bacteria Images
resolves10.1080/15257770.2019.1645851
Bacterial classification with convolutional neural networks based on different data reduction layers
resolves10.1016/j.snb.2020.127789
Single-cell classification of foodborne pathogens using hyperspectral microscope imaging coupled with deep learning frameworks
resolves10.1007/s00253-020-10387-4
Classification of foodborne bacteria using hyperspectral microscope imaging technology coupled with convolutional neural networks‡
resolves10.1007/978-981-15-5285-4_42
A 3D Convolutional Neural Network for Bacterial Image Classification
resolves10.1007/s11042-020-09284-9
Image-processing based taxonomy analysis of bacterial macromorphology using machine-learning models
The 14 references without a DOI — listed, not checked
no DOI — not checked“Microorganisms”, https, 23 Nov 2020 (online)
no DOI — not checked“Microbes”,https://www.sparknotes.com/biology/microorganisms/intro/summary/, 23 Nov 2020 (online)
no DOI — not checked“Bacteria”,https://microbiologysociety.org/why-microbiology-matters/what-is-microbiology/bacteria.html, 23 Nov2020 (online)
no DOI — not checked“Bacteria”, https://en.wikipedia.org/wiki/Bacteria, 23 Nov 2020 (online)
no DOI — not checkedKalaivaani P, Thangarajan DR (2020) Enhancing the classification accuracy in sentiment analysis with computational intelligence using joint sentiment topic detection with medlda. Intell Autom Soft Comput 26(1):71–79
no DOI — not checkedSandhya N, Charanjeet KR (2016) A review on machine learning techniques. Int J Recent Innov Trends Comput Commun 395–399
no DOI — not checkedPrasad S, Singh VK, Sapre A (2010) Handwriting Analysis based on Segmentation Method for Prediction of Human Personality using Support Vector Machine. Int J Comput Appl 8(12):25–29
no DOI — not checked“Algorithms of Machine Learning”, https://www.analyticsvidhya.com/blog/2017/09/common-machine-learning-algorithms, 7 Dec 2020 (online)
no DOI — not checkedMukherjee S, Kumar P, Saini R et al (2017) Plant disease identification using deep neural networks. J Multimed Inf Syst 6:233–238
no DOI — not checked“Ecological Importance of Bacteria”,https://en.wikipedia.org/wiki/Economic_importance_of_bacteria, 24 Nov 2020 (online)
no DOI — not checkedXiaojuan L, Cunshe C (2009) An improved BP neural network for wastewater bacteria recognition based on microscopic image analysis. WSEAS Trans Comput 8(2):237–247
no DOI — not checkedXiaojuan L, Cunshe C (2008) A novel wastewater recognition method based on microscopic image analysis. In: Proceedings of the 7th Conference on Circuits, Systems, Electronics, Control and Signal Processing, pp. 265–271
no DOI — not checkedhttp://misztal.edu.pl/software/database/dibas/
no DOI — not checked(2015) Bacteria images on HOWMED. [Online]. Available: http://howmed.net/microbiology. https://pi xnio.com/photos/science/microscopy_images
What this badge says. CiteStamped means the CHECKABLE references of this
work were clean at the dated check: each resolved to a known work in a public registry, and none
carried a retraction notice at that time. It says nothing about the quality, findings,
or importance of the work itself, and nothing about references deposited without a DOI.
checked 2026-07-25 — re-checked daily as this page is visited; titles and statuses come from Crossref and DataCite and are not part of the signed record
Embed this badge
Both snippets point at the live badge image and link back to this page. The badge re-renders from the daily check, so an embed never goes stale by more than a day of visits.
<a href="https://citestamp.com/citestamped/10.1007/s11831-021-09660-0"><img src="https://citestamp.com/citestamped/10.1007/s11831-021-09660-0/badge.svg" alt="CiteStamped reference-health badge" width="460" height="64"></a>
[](https://citestamp.com/citestamped/10.1007/s11831-021-09660-0)