Machine Learning and Deep Learning Based Computational Approaches in Automatic Microorganisms Image Recognition: Methodologies, Challenges, and Developments
TL;DR: A systematic review of research done using machine learning (ML) and deep leaning techniques in image recognition of different microorganisms is presented in this paper, which investigates certain research questions to analyze the studies concerning image pre-processing, feature extraction, classification techniques, evaluation measures, methodological limitations and technical development over a period of time.
read more
Abstract: Microorganisms or microbes comprise majority of the diversity on earth and are extremely important to human life. They are also integral to processes in the ecosystem. The process of their recognition is highly tedious, but very much essential in microbiology to carry out different experimentation. To overcome certain challenges, machine learning techniques assist microbiologists in automating the entire process. This paper presents a systematic review of research done using machine learning (ML) and deep leaning techniques in image recognition of different microorganisms. This review investigates certain research questions to analyze the studies concerning image pre-processing, feature extraction, classification techniques, evaluation measures, methodological limitations and technical development over a period of time. In addition to this, this paper also addresses the certain challenges faced by researchers in this field. Total of 100 research publications in the chronological order of their appearance have been considered for the time period 1995-2021. This review will be extremely beneficial to the researchers due to the detailed analysis of different methodologies and comprehensive overview of effectiveness of different ML techniques being applied in microorganism image recognition field.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Machine Learning-enabled Optimization of Extrusion-based 3D Printing.
TL;DR: In this article , the authors presented the first integration of ML and 3D printing through an easy-to-use graphical user interface (GUI) for printing parameter optimization, which enables users to simply upload a design (desired to print) to the GUI along with desired printing temperature and pressure.
40
Machine learning for microalgae detection and utilization
TL;DR: The paper summarizes recent advances based on various machine learning algorithms in microalgae applications, such asmicroalgae classification, bioenergy generation fromMicroalgae, environment purification with micro algae, and microalgai growth monitor, and prospect development of machine learning algorithm in micro algae treatment in the future are prospected.
Trends in digital image processing of isolated microalgae by incorporating classification algorithm.
TL;DR: In this article , the authors highlight the potential innovations in digital microscopy with the incorporation of both hardware and software that can produce a reliable recognition, detection, enumeration, and real-time acquisition of microalgae species.
37
Mitigating spread of contamination in meat supply chain management using deep learning
TL;DR: In this article , a classification model was trained by the DCNN and PSO algorithms with 100% accuracy, which discerns wholesome meat from spoiled ones to prevent cross-contamination with food-borne pathogens.
Developments in Image Processing Using Deep Learning and Reinforcement Learning
Jorge Valente,Joao Bras Antonio,Carlos Mora,Sandra Jardim +3 more
TL;DR: This study conducted a comprehensive survey regarding advances in AI design and the optimization solutions proposed to deal with image processing challenges and proposes future research directions in this field of constant and fast evolution.
33
References
HyperFace: A Deep Multi-Task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognition
TL;DR: HyperFace as discussed by the authors combines face detection, landmarks localization, pose estimation and gender recognition using deep convolutional neural networks (CNNs) and achieves significant improvement in performance by fusing intermediate layers of a deep CNN using a separate CNN followed by a multi-task learning algorithm that operates on the fused features.
1.5K
Advances in Chemical and Biological Methods to Identify Microorganisms-From Past to Present
Ricardo Franco-Duarte,Lucia Černáková,Snehal Kadam,Karishma S. Kaushik,Bahare Salehi,Antonio Bevilacqua,Maria Rosaria Corbo,Hubert Antolak,Katarzyna Dybka-Stępień,Martyna Leszczewicz,Saulo R. Tintino,Veruska Cintia Alexandrino de Souza,Javad Sharifi-Rad,Henrique Douglas Melo Coutinho,Natália Martins,Célia F. Rodrigues +15 more
- 13 May 2019
TL;DR: The goal of this review is to present the past and the present methods of detection and identification of microorganisms, and to discuss their advantages and their limitations.
384
Deep convolution neural network for image recognition
TL;DR: This paper considers cholera and malaria epidemics for microscopic images classification with a relevant CNN, respectively Vibrio cholerae images and Plasmodium falciparum images and proposes a methodology based on efficient Convolution Neural Network architecture in order to classify epidemic pathogen.
361
A PSO based Energy Efficient Coverage Control Algorithm for Wireless Sensor Networks
TL;DR: A novel coverage control algorithm based on Particle Swarm Optimization (PSO) is presented that can effectively improve coverage rate and reduce energy consumption in WSNs.
341
A Novel Image Classification Approach via Dense-MobileNet Models
TL;DR: Dense blocks that are proposed in DenseNets are introduced into MobileNet in order to further reduce the number of network parameters and improve the classification accuracy, and two Dense-MobileNet models are designed.