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Kianian I, Mottaqi M, Mohammadipanah F, Sajedi H. Automated identification of toxigenic cyanobacterial genera for water quality control purposes. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2024; 362:121274. [PMID: 38838537 DOI: 10.1016/j.jenvman.2024.121274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 04/27/2024] [Accepted: 05/26/2024] [Indexed: 06/07/2024]
Abstract
Cyanobacteria are the dominating microorganisms in aquatic environments, posing significant risks to public health due to toxin production in drinking water reservoirs. Traditional water quality assessments for abundance of the toxigenic genera in water samples are both time-consuming and error-prone, highlighting the urgent need for a fast and accurate automated approach. This study addresses this gap by introducing a novel public dataset, TCB-DS (Toxigenic Cyanobacteria Dataset), comprising 2593 microscopic images of 10 toxigenic cyanobacterial genera and subsequently, an automated system to identify these genera which can be divided into two parts. Initially, a feature extractor Convolutional Neural Network (CNN) model was employed, with MobileNet emerging as the optimal choice after comparing it with various other popular architectures such as MobileNetV2, VGG, etc. Secondly, to perform classification algorithms on the extracted features of the first section, multiple approaches were tested and the experimental results indicate that a Fully Connected Neural Network (FCNN) had the optimal performance with weighted accuracy and f1-score of 94.79% and 94.91%, respectively. The highest macro accuracy and f1-score were 90.17% and 87.64% which were acquired using MobileNetV2 as the feature extractor and FCNN as the classifier. These results demonstrate that the proposed approach can be employed as an automated screening tool for identifying toxigenic Cyanobacteria with practical implications for water quality control replacing the traditional estimation given by the lab operator following microscopic observations. The dataset and code of this paper are publicly available at https://github.com/iman2693/CTCB.
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Affiliation(s)
- Iman Kianian
- Department of Computer Science, School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, Iran.
| | | | - Fatemeh Mohammadipanah
- Department of Microbial Biotechnology, School of Biology and Center of Excellence in Phylogeny of Living Organisms, College of Science, University of Tehran, 14155-6455, Tehran, Iran.
| | - Hedieh Sajedi
- Department of Computer Science, School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, Iran.
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2
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Chong JWR, Khoo KS, Chew KW, Ting HY, Show PL. Trends in digital image processing of isolated microalgae by incorporating classification algorithm. Biotechnol Adv 2023; 63:108095. [PMID: 36608745 DOI: 10.1016/j.biotechadv.2023.108095] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Revised: 12/17/2022] [Accepted: 01/01/2023] [Indexed: 01/05/2023]
Abstract
Identification of microalgae species is of importance due to the uprising of harmful algae blooms affecting both the aquatic habitat and human health. Despite this occurence, microalgae have been identified as a green biomass and alternative source due to its promising bioactive compounds accumulation that play a significant role in many industrial applications. Recently, microalgae species identification has been conducted through DNA analysis and various microscopy techniques such as light, scanning electron, transmission electron, and atomic force -microscopy. The aforementioned procedures have encouraged researchers to consider alternate ways due to limitations such as costly validation, requiring skilled taxonomists, prolonged analysis, and low accuracy. This review highlights 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. Several steps such as image acquisition, processing, feature extraction, and selection are discussed, for the purpose of generating high image quality by removing unwanted artifacts and noise from the background. These steps of identification of microalgae species is performed by reliable image classification through machine learning as well as deep learning algorithms such as artificial neural networks, support vector machines, and convolutional neural networks. Overall, this review provides comprehensive insights into numerous possibilities of microalgae image identification, image pre-processing, and machine learning techniques to address the challenges in developing a robust digital classification tool for the future.
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Affiliation(s)
- Jun Wei Roy Chong
- Zhejiang Provincial Key Laboratory for Subtropical Water Environment and Marine Biological Resources Protection, Wenzhou University, Wenzhou 325035, China; Department of Chemical and Environmental Engineering, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, Semenyih 43500, Selangor Darul Ehsan, Malaysia
| | - Kuan Shiong Khoo
- Department of Chemical Engineering and Materials Science, Yuan Ze University, Taoyuan, Taiwan.
| | - Kit Wayne Chew
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, 62 Nanyang Drive, Singapore, 637459 Singapore
| | - Huong-Yong Ting
- Drone Research and Application Centre, University of Technology Sarawak, No.1, Jalan Universiti, 96000 Sibu, Sarawak, Malaysia
| | - Pau Loke Show
- Zhejiang Provincial Key Laboratory for Subtropical Water Environment and Marine Biological Resources Protection, Wenzhou University, Wenzhou 325035, China; Department of Chemical and Environmental Engineering, Faculty of Science and Engineering, University of Nottingham Malaysia, Jalan Broga, Semenyih 43500, Selangor Darul Ehsan, Malaysia; Department of Sustainable Engineering, Saveetha School of Engineering, SIMATS, Chennai 602105, India.
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3
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Chong JWR, Khoo KS, Chew KW, Vo DVN, Balakrishnan D, Banat F, Munawaroh HSH, Iwamoto K, Show PL. Microalgae identification: Future of image processing and digital algorithm. BIORESOURCE TECHNOLOGY 2023; 369:128418. [PMID: 36470491 DOI: 10.1016/j.biortech.2022.128418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Revised: 11/08/2022] [Accepted: 11/11/2022] [Indexed: 06/17/2023]
Abstract
The identification of microalgae species is an important tool in scientific research and commercial application to prevent harmful algae blooms (HABs) and recognizing potential microalgae strains for the bioaccumulation of valuable bioactive ingredients. The aim of this study is to incorporate rapid, high-accuracy, reliable, low-cost, simple, and state-of-the-art identification methods. Thus, increasing the possibility for the development of potential recognition applications, that could identify toxic-producing and valuable microalgae strains. Recently, deep learning (DL) has brought the study of microalgae species identification to a much higher depth of efficiency and accuracy. In doing so, this review paper emphasizes the significance of microalgae identification, and various forms of machine learning algorithms for image classification, followed by image pre-processing techniques, feature extraction, and selection for further classification accuracy. Future prospects over the challenges and improvements of potential DL classification model development, application in microalgae recognition, and image capturing technologies are discussed accordingly.
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Affiliation(s)
- Jun Wei Roy Chong
- Department of Chemical and Environmental Engineering, Faculty of Science and Engineering, University of Nottingham Malaysia Campus, Jalan Broga, Semenyih 43500, Selangor Darul Ehsan, Malaysia
| | - Kuan Shiong Khoo
- Department of Chemical Engineering and Materials Science, Yuan Ze University, Taoyuan, Taiwan; Centre for Research and Graduate Studies, University of Cyberjaya, Persiaran Bestari, 63000 Cyberjaya, Selangor, Malaysia
| | - Kit Wayne Chew
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, 62 Nanyang Drive, Singapore, 637459, Singapore
| | - Dai-Viet N Vo
- Institute of Applied Technology and Sustainable Development, Nguyen Tat Thanh University, Ho Chi Minh City 755414, Vietnam
| | - Deepanraj Balakrishnan
- Department of Mechanical Engineering, College of Engineering, Prince Mohammad Bin Fahd University, Al Khobar, 31952, Saudi Arabia
| | - Fawzi Banat
- Department of Chemical Engineering, Khalifa University, P.O Box 127788, Abu Dhabi, United Arab Emirates
| | - Heli Siti Halimatul Munawaroh
- Study Program of Chemistry, Department of Chemistry Education, Universitas Pendidikan Indonesia, Bandung 40154, West Java, Indonesia
| | - Koji Iwamoto
- Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, 54100, Kuala Lumpur, Malaysia
| | - Pau Loke Show
- Department of Chemical and Environmental Engineering, Faculty of Science and Engineering, University of Nottingham Malaysia Campus, Jalan Broga, Semenyih 43500, Selangor Darul Ehsan, Malaysia; Zhejiang Provincial Key Laboratory for Subtropical Water Environment and Marine Biological Resources Protection, Wenzhou University, Wenzhou 325035, China; Department of Sustainable Engineering, Saveetha School of Engineering, SIMATS, Chennai 602105, India.
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4
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Gong X, Ma C, Sun B, Zhang J. An Efficient Self-Organized Detection System for Algae. SENSORS (BASEL, SWITZERLAND) 2023; 23:1609. [PMID: 36772648 PMCID: PMC9920197 DOI: 10.3390/s23031609] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 01/25/2023] [Accepted: 01/31/2023] [Indexed: 06/18/2023]
Abstract
Algal blooms have seriously affected the production and life of people and real-time detection of algae in water samples is a powerful measure to prevent algal blooms. The traditional manual detection of algae with a microscope is extremely time-consuming. In recent years, although there have been many studies using deep learning to classify and detect algae, most of them have focused on the relatively simple task of algal classification. In addition, some existing algal detection studies not only use small datasets containing limited algal species, but also only prove that object detection algorithms can be applied to algal detection tasks. These studies cannot implement the real-time detection of algae and timely warning of algal blooms. Therefore, this paper proposes an efficient self-organized detection system for algae. Benefiting from this system, we propose an interactive method to generate the algal detection dataset containing 28,329 images, 562,512 bounding boxes and 54 genera. Then, based on this dataset, we not only explore and compare the performance of 10 different versions of state-of-the-art object detection algorithms for algal detection, but also tune the detection system we built to its optimum state. In practical application, the system not only has good algal detection results, but also can complete the scanning, photographing and detection of a 2 cm × 2 cm, 0.1 mL algal slide specimen within five minutes (the resolution is 0.25886 μm/pixel); such a task requires a well-trained algal expert to work continuously for more than three hours. The efficient algal self-organized detection system we built makes it possible to detect algae in real time. In the future, with the help of IoT, we can use various smart sensors, actuators and intelligent controllers to achieve real-time collection and wireless transmission of algal data, use the efficient algal self-organized detection system we built to implement real-time algal detection and upload the detection results to the cloud to realize timely warning of algal blooms.
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Affiliation(s)
- Xingrui Gong
- School of Computer Science and Engineering, Faculty of Innovation Engineering, Macau University of Science and Technology, Macau 999078, China
| | - Chao Ma
- Department of Engineering Science, Faculty of Innovation Engineering, Macau University of Science and Technology, Macau 999078, China
| | - Beili Sun
- Jiangsu Metabio Science & Technology Co., Ltd., Wuxi 214028, China
- Wuxi Key Laboratory of Biochips, Southeast University Wuxi Branch, Wuxi 214135, China
| | - Junyi Zhang
- Jiangsu Wuxi Environmental Monitoring Center, Wuxi 214121, China
- School of Environmental and Civil Engineering, Jiangnan University, Wuxi 214122, China
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5
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Chen Y, Bian S, Liu Y, Zhang Z. A novel method based on neural architecture search for Diptera insect classification on embedded devices. ECOL INFORM 2022. [DOI: 10.1016/j.ecoinf.2022.101791] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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6
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Computer Vision Based Deep Learning Approach for the Detection and Classification of Algae Species Using Microscopic Images. WATER 2022. [DOI: 10.3390/w14142219] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The natural phenomenon of harmful algae bloom (HAB) has a bad impact on the quality of pure and freshwater. It increases the risk to human health, water bodies and overall aquatic ecosystem. It is necessary to continuously monitor and perform proper action against HAB. The inspection of algae blooms by using conventional methods, like algae detection under microscopes, is a difficult, expensive, and time-consuming task, however, computer vision-based deep learning models play a vital role in identifying and detecting harmful algae growth in aquatic ecosystems and water reservoirs. Many studies have been conducted to address harmful algae growth by using a CNN based model, however, the YOLO model is considered more accurate in identifying the algae. This advanced deep learning method is extensively used to detect algae and classify them according to their corresponding category. In this study, we used various versions of the convolution neural network (CNN) based on the You Only Look Once (YOLO) model. Recently YOLOv5 has been getting more attention due to its performance in real-time object detection. We performed a series of experiments on our custom microscopic images dataset by using YOLOv3, YOLOv4, and YOLOv5 to detect and classify the harmful algae bloom (HAB) of four classes. We used pre-processing techniques to enhance the quantity of data. The mean average precision (mAP) of YOLOv3, YOLOv4, and YOLO v5 is 75.3%, 83.0%, and 91.0% respectively. For the monitoring of algae bloom in freshwater, computer-aided based systems are very helpful and effective. To the best of our knowledge, this work is pioneering in the AI community for applying the YOLO models to detect algae and classify from microscopic images.
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7
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Novel Approach to Freshwater Diatom Profiling and Identification Using Raman Spectroscopy and Chemometric Analysis. WATER 2022. [DOI: 10.3390/w14132116] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
(1) An approach with great potential for fast and cost-effective profiling and identification of diatoms in lake ecosystems is presented herein. This approach takes advantage of Raman spectroscopy. (2) The study was based on the analysis of 790 Raman spectra from 29 species, belonging to 15 genera, 12 families, 9 orders and 4 subclasses, which were analysed using chemometric methods. The Raman data were first analysed by a partial least squares regression discriminant analysis (PLS-DA) to characterise the diatom species. Furthermore, a method was developed to streamline the integrated interpretation of PLS-DA when a high number of significant components is extracted. Subsequently, an artificial neural network (ANN) was used for taxa identification from Raman data. (3) The PLS interpretation produced a Raman profile for each species reflecting its biochemical composition. The ANN models were useful to identify various taxa with high accuracy. (4) Compared to studies in the literature, involving huge datasets one to four orders of magnitude larger than ours, high sensitivity was found for the identification of Achnanthidium exiguum (67%), Fragilaria pararumpens (67%), Amphora pediculus (71%), Achnanthidium minutissimum (80%) and Melosira varians (82%).
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8
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Vaughan L, Zamyadi A, Ajjampur S, Almutaram H, Freguia S. A review of microscopic cell imaging and neural network recognition for synergistic cyanobacteria identification and enumeration. ANAL SCI 2022; 38:261-279. [PMID: 35286640 PMCID: PMC8938360 DOI: 10.1007/s44211-021-00013-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 10/07/2021] [Indexed: 11/30/2022]
Abstract
Real-time cyanobacteria/algal monitoring is a valuable tool for early detection of harmful algal blooms, water treatment efficacy evaluation, and assists tailored water quality risk assessments by considering taxonomy and cell counts. This review evaluates and proposes a synergistic approach using neural network image recognition and microscopic imaging devices by first evaluating published literature for both imaging microscopes and image recognition. Quantitative phase imaging was considered the most promising of the investigated imaging techniques due to the provision of enhanced information relative to alternatives. This information provides significant value to image recognition neural networks, such as the convolutional neural networks discussed within this review. Considering published literature, a cyanobacteria monitoring system and corresponding image processing workflow using in situ sample collection buoys and on-shore sample processing was proposed. This system can be implemented using commercially available equipment to facilitate accurate, real-time water quality monitoring.
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Affiliation(s)
- Liam Vaughan
- Water Research Australia, Level 2, 250 Victoria Square, Adelaide, SA, 5000, Australia.
| | - Arash Zamyadi
- Water Research Australia, Level 2, 250 Victoria Square, Adelaide, SA, 5000, Australia
- Department of Chemical Engineering, The University of Melbourne, Parkville, VIC, 3010, Australia
| | - Suraj Ajjampur
- Water Research Australia, Level 2, 250 Victoria Square, Adelaide, SA, 5000, Australia
| | - Husein Almutaram
- Department of Civil and Mineral Engineering, University of Toronto, Toronto, ON, M5S 1A4, Canada
| | - Stefano Freguia
- Department of Chemical Engineering, The University of Melbourne, Parkville, VIC, 3010, Australia
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9
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Hong SM, Baek SS, Yun D, Kwon YH, Duan H, Pyo J, Cho KH. Monitoring the vertical distribution of HABs using hyperspectral imagery and deep learning models. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 794:148592. [PMID: 34217087 DOI: 10.1016/j.scitotenv.2021.148592] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 05/13/2021] [Accepted: 06/17/2021] [Indexed: 06/13/2023]
Abstract
Remote sensing techniques have been applied to monitor the spatiotemporal variation of harmful algal blooms (HABs) in many inland waters. However, these studies have been limited to monitor the vertical distribution of HABs due to the optical complexity of inland water. Therefore, this study applied a deep neural network model to monitor the vertical distribution of Chlorophyll-a (Chl-a), phycocyanin (PC), and turbidity (Turb) using drone-borne hyperspectral imagery, in-situ measurement, and meteoroidal data. The pigment concentrations were measured between depths of 0 m and 5.0 m with 0.05 m intervals. Here, four state-of-the-art data-driven model structures (ResNet-18, ResNet-101, GoogLeNet, and Inception v3) were adopted for estimating the vertical distributions of the harmful algal pigments. Among the four models, the ResNet-18 model showed the best performance, with an R2 value of 0.70. In addition, Gradient-weighted Class Activation Mapping (Grad-CAM) substantially provided informative reflectance band ranges near 490 nm and 620 nm in the hyperspectral image for the vertical estimation of pigments. Therefore, this study demonstrated that the explainable deep learning model with drone-borne hyperspectral images has the potential to estimate Chl-a, PC, and Turb vertical distributions and to show influential features that contribute to describing the vertical profile phenomena.
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Affiliation(s)
- Seok Min Hong
- School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan 689-798, Republic of Korea
| | - Sang-Soo Baek
- School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan 689-798, Republic of Korea
| | - Daeun Yun
- School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan 689-798, Republic of Korea
| | - Yong-Hwan Kwon
- Electronics and Telecommunication Research Institute, 218 Gajeong-ro, Yeseong-gu, Daejeon 305-700, Republic of Korea
| | - Hongtao Duan
- Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China
| | - JongCheol Pyo
- Center for Environmental Data Strategy, Korea Environment Institute, Sejong 30147, Republic of Korea.
| | - Kyung Hwa Cho
- School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan 689-798, Republic of Korea.
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10
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Barsanti L, Birindelli L, Gualtieri P. Water monitoring by means of digital microscopy identification and classification of microalgae. ENVIRONMENTAL SCIENCE. PROCESSES & IMPACTS 2021; 23:1443-1457. [PMID: 34549767 DOI: 10.1039/d1em00258a] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Marine and freshwater microalgae belong to taxonomically and morphologically diverse groups of organisms spanning many phyla with thousands of species. These organisms play an important role as indicators of water ecosystem conditions since they react quickly and predictably to a broad range of environmental stressors, thus providing early signals of dangerous changes. Traditionally, microscopic analysis has been used to identify and enumerate different types of organisms present within a given environment at a given point in time. However, this approach is both time-consuming and labor intensive, as it relies on manual processing and classification of planktonic organisms present within collected water samples. Furthermore, it requires highly skilled specialists trained to recognize and distinguish one taxa from another on the basis of often subtle morphological differences. Given these restrictions, a considerable amount of effort has been recently funneled into automating different steps of both the sampling and classification processes, making it possible to generate previously unprecedented volumes of plankton image data and obtain an essential database to analyze the composition of plankton assemblages. In this review we report state-of-the-art methods used for automated plankton classification by means of digital microscopy. The computer-microscope system hardware and the image processing techniques used for recognition and classification of planktonic organisms (segmentation, shape feature extraction, pigment signature determination and neural network grouping) will be described. An introduction and overview of the topic, its current state and indications of future directions the field is expected to take will be provided, organizing the review for both experts and researchers new to the field.
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Affiliation(s)
- Laura Barsanti
- CNR, Istituto di Biofisica, Via Moruzzi 1, Pisa, 56124, Italy.
| | | | - Paolo Gualtieri
- CNR, Istituto di Biofisica, Via Moruzzi 1, Pisa, 56124, Italy.
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11
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Carloto I, Johnston P, Pestana CJ, Lawton LA. Detection of morphological changes caused by chemical stress in the cyanobacterium Planktothrix agardhii using convolutional neural networks. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 784:146956. [PMID: 33894604 DOI: 10.1016/j.scitotenv.2021.146956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 03/31/2021] [Accepted: 03/31/2021] [Indexed: 06/12/2023]
Abstract
The presence of harmful algal bloom in many reservoirs around the world, alongside the lack of sanitation law/ordinance regarding cyanotoxin monitoring (particularly in developing countries), create a scenario in which the local population could potentially chronically consume cyanotoxin-contaminated waters. Therefore, it is crucial to develop low cost tools to detect possible systems failures and consequent toxin release inferred by morphological changes of cyanobacteria in the raw water. This paper aimed to look for the best combination of convolutional neural network (CNN), optimizer and image segmentation technique to differentiate P. agardhii trichomes before and after chemical stress caused by the addition of hydrogen peroxide. This method takes a step towards accurate monitoring of cyanobacteria in the field without the need for a mobile lab. After testing three different network architectures (AlexNet, 3ConvLayer and 2ConvLayer), four different optimizers (Adam, Adagrad, RMSProp and SDG) and five different image segmentations methods (Canny Edge Detection, Morphological Filter, HP filter, GrabCut and Watershed), the combination 2ConvLayer with Adam optimizer and GrabCut segmentation, provided the highest median accuracy (93.33%) for identifying H2O2-induced morphological changes in P. agardhii. Our results emphasize the fact that the trichome classification problem can be adequately tackled with a limited number of learned features due to the lack of complexity in micrographs from before and after chemical stress. To the authors' knowledge, this is the first time that CNNs were applied to detect morphological changes in cyanobacteria caused by chemical stress. Thus, it is a significant step forward in developing low cost tools based on image recognition, to shield water consumers, especially in the poorest regions, against cyanotoxin-contaminated water.
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Affiliation(s)
- Ismael Carloto
- School of Pharmacy and Life Sciences, Robert Gordon University, Garthdee Road, Aberdeen AB10 7GJ, UK.
| | - Pamela Johnston
- School of Computing, Robert Gordon University, Garthdee Road, Aberdeen AB10 7GJ, UK.
| | - Carlos J Pestana
- School of Pharmacy and Life Sciences, Robert Gordon University, Garthdee Road, Aberdeen AB10 7GJ, UK.
| | - Linda A Lawton
- School of Pharmacy and Life Sciences, Robert Gordon University, Garthdee Road, Aberdeen AB10 7GJ, UK.
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12
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Dhindsa A, Bhatia S, Agrawal S, Sohi BS. An Improvised Machine Learning Model Based on Mutual Information Feature Selection Approach for Microbes Classification. ENTROPY (BASEL, SWITZERLAND) 2021; 23:257. [PMID: 33672252 PMCID: PMC7927045 DOI: 10.3390/e23020257] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Revised: 02/10/2021] [Accepted: 02/20/2021] [Indexed: 12/11/2022]
Abstract
The accurate classification of microbes is critical in today's context for monitoring the ecological balance of a habitat. Hence, in this research work, a novel method to automate the process of identifying microorganisms has been implemented. To extract the bodies of microorganisms accurately, a generalized segmentation mechanism which consists of a combination of convolution filter (Kirsch) and a variance-based pixel clustering algorithm (Otsu) is proposed. With exhaustive corroboration, a set of twenty-five features were identified to map the characteristics and morphology for all kinds of microbes. Multiple techniques for feature selection were tested and it was found that mutual information (MI)-based models gave the best performance. Exhaustive hyperparameter tuning of multilayer layer perceptron (MLP), k-nearest neighbors (KNN), quadratic discriminant analysis (QDA), logistic regression (LR), and support vector machine (SVM) was done. It was found that SVM radial required further improvisation to attain a maximum possible level of accuracy. Comparative analysis between SVM and improvised SVM (ISVM) through a 10-fold cross validation method ultimately showed that ISVM resulted in a 2% higher performance in terms of accuracy (98.2%), precision (98.2%), recall (98.1%), and F1 score (98.1%).
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Affiliation(s)
- Anaahat Dhindsa
- Department of Electronics and Communication Engineering, Chandigarh University, Gharuan, Punjab 140413, India;
- University Institute of Engineering and Technology, Panjab University, Chandigarh 160014, India;
| | - Sanjay Bhatia
- Post Graduate Department of Zoology, University of Jammu, Kashmir 180006, India;
| | - Sunil Agrawal
- University Institute of Engineering and Technology, Panjab University, Chandigarh 160014, India;
| | - Balwinder Singh Sohi
- Department of Electronics and Communication Engineering, Chandigarh University, Gharuan, Punjab 140413, India;
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13
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Luo S, Nguyen KT, Nguyen BTT, Feng S, Shi Y, Elsayed A, Zhang Y, Zhou X, Wen B, Chierchia G, Talbot H, Bourouina T, Jiang X, Liu AQ. Deep learning-enabled imaging flow cytometry for high-speed Cryptosporidium and Giardia detection. Cytometry A 2021; 99:1123-1133. [PMID: 33550703 DOI: 10.1002/cyto.a.24321] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Revised: 02/01/2021] [Accepted: 02/03/2021] [Indexed: 12/19/2022]
Abstract
Imaging flow cytometry has become a popular technology for bioparticle image analysis because of its capability of capturing thousands of images per second. Nevertheless, the vast number of images generated by imaging flow cytometry imposes great challenges for data analysis especially when the species have similar morphologies. In this work, we report a deep learning-enabled high-throughput system for predicting Cryptosporidium and Giardia in drinking water. This system combines imaging flow cytometry and an efficient artificial neural network called MCellNet, which achieves a classification accuracy >99.6%. The system can detect Cryptosporidium and Giardia with a sensitivity of 97.37% and a specificity of 99.95%. The high-speed analysis reaches 346 frames per second, outperforming the state-of-the-art deep learning algorithm MobileNetV2 in speed (251 frames per second) with a comparable classification accuracy. The reported system empowers rapid, accurate, and high throughput bioparticle detection in clinical diagnostics, environmental monitoring and other potential biosensing applications.
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Affiliation(s)
- Shaobo Luo
- ESIEE, Universite Paris-Est, Noisy-le-Grand Cedex, France.,Nanyang Environment and Water Research Institute, Nanyang Technological University, Singapore, Singapore
| | - Kim Truc Nguyen
- Nanyang Environment and Water Research Institute, Nanyang Technological University, Singapore, Singapore.,School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore, Singapore
| | - Binh T T Nguyen
- School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore, Singapore
| | - Shilun Feng
- Nanyang Environment and Water Research Institute, Nanyang Technological University, Singapore, Singapore.,School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore, Singapore
| | - Yuzhi Shi
- School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore, Singapore
| | - Ahmed Elsayed
- ESIEE, Universite Paris-Est, Noisy-le-Grand Cedex, France
| | - Yi Zhang
- School of Mechanical & Aerospace Engineering, Nanyang Technological University, Singapore, Singapore
| | - Xiaohong Zhou
- Research Centre of Environmental and Health Sensing Technology, School of Environment, Tsinghua University, Beijing, China
| | - Bihan Wen
- School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore, Singapore
| | | | - Hugues Talbot
- CentraleSupelec, Universite Paris-Saclay, Saint-Aubin, France
| | | | - Xudong Jiang
- School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore, Singapore
| | - Ai Qun Liu
- Nanyang Environment and Water Research Institute, Nanyang Technological University, Singapore, Singapore.,School of Electrical & Electronic Engineering, Nanyang Technological University, Singapore, Singapore
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14
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Yadav D, Jalal A, Garlapati D, Hossain K, Goyal A, Pant G. Deep learning-based ResNeXt model in phycological studies for future. ALGAL RES 2020. [DOI: 10.1016/j.algal.2020.102018] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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15
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Krause LMK, Koc J, Rosenhahn B, Rosenhahn A. Fully Convolutional Neural Network for Detection and Counting of Diatoms on Coatings after Short-Term Field Exposure. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2020; 54:10022-10030. [PMID: 32663392 DOI: 10.1021/acs.est.0c01982] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
While the use of deep learning is a valuable technology for automatic detection systems for medical data and images, the biofouling community is still lacking an analytical tool for the detection and counting of diatoms on samples after short-term field exposure. In this work, a fully convolutional neural network was implemented as a fast and simple approach to detect diatoms on two-channel (fluorescence and phase-contrast) microscopy images by predicting bounding boxes. The developed approach performs well with only a small number of trainable parameters and a F1 score of 0.82. Counting diatoms was evaluated on a data set of 600 microscopy images of three different surface chemistries (hydrophilic and hydrophobic) and is very similar to counting by humans while demanding only a fraction of the analysis time.
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Affiliation(s)
- Lutz M K Krause
- Analytical Chemistry-Biointerfaces, Ruhr University Bochum, 44801 Bochum, Germany
| | - Julian Koc
- Analytical Chemistry-Biointerfaces, Ruhr University Bochum, 44801 Bochum, Germany
| | - Bodo Rosenhahn
- Institute for Information Processing, Leibniz University Hannover, 30167 Hannover, Germany
| | - Axel Rosenhahn
- Analytical Chemistry-Biointerfaces, Ruhr University Bochum, 44801 Bochum, Germany
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16
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Investigation of the Influence of Excess Pumping on Groundwater Salinity in the Gaza Coastal Aquifer (Palestine) Using Three Predicted Future Scenarios. WATER 2020. [DOI: 10.3390/w12082218] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The Gaza coastal aquifer (GCA) is the only source of water for about two million citizens living in Gaza Strip, Palestine. The groundwater quality in GCA has deteriorated rapidly due to many factors. The most crucial factor is the excess pumping due to the high population density. The objective of this article was to evaluate the influence of excess pumping on GCA’s salinity using 10-year predicted future scenarios based on artificial neural networks (ANNs). The ANN-based model was generated to predict the GCA’s salinity for three future scenarios that were designed based on different pumping rates. The results showed that when the pumping rate remains at the present conditions, salinity will increase rapidly in most GCA areas, and the availability of fresh water will decrease in disquieting rates by 2030. Only about 8% of the overall GCA’s area is expected to stay within 500 mg/L of the chloride concentration. Results also indicate that salinity would be improved slightly if the pumping rate is kept at 50% of the current pumping rates while the improvement rate is much faster if the pumping is stopped completely, which is an unfeasible scenario. The results are considered as an urgent call for developing an integrated water management strategy aiming at improving GCA quality by providing other drinking water resources to secure the increasing water demand.
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17
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Qian P, Zhao Z, Liu H, Wang Y, Peng Y, Hu S, Zhang J, Deng Y, Zeng Z. Multi-Target Deep Learning for Algal Detection and Classification. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:1954-1957. [PMID: 33018385 DOI: 10.1109/embc44109.2020.9176204] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Water quality has a direct impact on industry, agriculture, and public health. Algae species are common indicators of water quality. It is because algal communities are sensitive to changes in their habitats, giving valuable knowledge on variations in water quality. However, water quality analysis requires professional inspection of algal detection and classification under microscopes, which is very time-consuming and tedious. In this paper, we propose a novel multi-target deep learning framework for algal detection and classification. Extensive experiments were carried out on a large-scale colored microscopic algal dataset. Experimental results demonstrate that the proposed method leads to the promising performance on algal detection, class identification and genus identification.
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18
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Pant G, Yadav D, Gaur A. ResNeXt convolution neural network topology-based deep learning model for identification and classification of Pediastrum. ALGAL RES 2020. [DOI: 10.1016/j.algal.2020.101932] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
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