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Feng R, Li S, Zhang Y. AI-powered microscopy image analysis for parasitology: integrating human expertise. Trends Parasitol 2024; 40:633-646. [PMID: 38824067 DOI: 10.1016/j.pt.2024.05.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Revised: 05/06/2024] [Accepted: 05/07/2024] [Indexed: 06/03/2024]
Abstract
Microscopy image analysis plays a pivotal role in parasitology research. Deep learning (DL), a subset of artificial intelligence (AI), has garnered significant attention. However, traditional DL-based methods for general purposes are data-driven, often lacking explainability due to their black-box nature and sparse instructional resources. To address these challenges, this article presents a comprehensive review of recent advancements in knowledge-integrated DL models tailored for microscopy image analysis in parasitology. The massive amounts of human expert knowledge from parasitologists can enhance the accuracy and explainability of AI-driven decisions. It is expected that the adoption of knowledge-integrated DL models will open up a wide range of applications in the field of parasitology.
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Affiliation(s)
- Ruijun Feng
- College of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China; School of Computer Science and Engineering, University of New South Wales, Sydney, Australia
| | - Sen Li
- College of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China
| | - Yang Zhang
- College of Science, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong 518055, China.
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2
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Huang X, Rymbekova A, Dolgova O, Lao O, Kuhlwilm M. Harnessing deep learning for population genetic inference. Nat Rev Genet 2024; 25:61-78. [PMID: 37666948 DOI: 10.1038/s41576-023-00636-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/11/2023] [Indexed: 09/06/2023]
Abstract
In population genetics, the emergence of large-scale genomic data for various species and populations has provided new opportunities to understand the evolutionary forces that drive genetic diversity using statistical inference. However, the era of population genomics presents new challenges in analysing the massive amounts of genomes and variants. Deep learning has demonstrated state-of-the-art performance for numerous applications involving large-scale data. Recently, deep learning approaches have gained popularity in population genetics; facilitated by the advent of massive genomic data sets, powerful computational hardware and complex deep learning architectures, they have been used to identify population structure, infer demographic history and investigate natural selection. Here, we introduce common deep learning architectures and provide comprehensive guidelines for implementing deep learning models for population genetic inference. We also discuss current challenges and future directions for applying deep learning in population genetics, focusing on efficiency, robustness and interpretability.
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Affiliation(s)
- Xin Huang
- Department of Evolutionary Anthropology, University of Vienna, Vienna, Austria.
- Human Evolution and Archaeological Sciences (HEAS), University of Vienna, Vienna, Austria.
| | - Aigerim Rymbekova
- Department of Evolutionary Anthropology, University of Vienna, Vienna, Austria
- Human Evolution and Archaeological Sciences (HEAS), University of Vienna, Vienna, Austria
| | - Olga Dolgova
- Integrative Genomics Laboratory, CIC bioGUNE - Centro de Investigación Cooperativa en Biociencias, Derio, Biscaya, Spain
| | - Oscar Lao
- Institute of Evolutionary Biology, CSIC-Universitat Pompeu Fabra, Barcelona, Spain.
| | - Martin Kuhlwilm
- Department of Evolutionary Anthropology, University of Vienna, Vienna, Austria.
- Human Evolution and Archaeological Sciences (HEAS), University of Vienna, Vienna, Austria.
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3
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Phelan JE, Turkiewicz A, Manko E, Thorpe J, Vanheer LN, van de Vegte-Bolmer M, Ngoc NTH, Binh NTH, Thieu NQ, Gitaka J, Nolder D, Beshir KB, Dombrowski JG, Di Santi SM, Bousema T, Sutherland CJ, Campino S, Clark TG. Rapid profiling of Plasmodium parasites from genome sequences to assist malaria control. Genome Med 2023; 15:96. [PMID: 37950308 PMCID: PMC10636944 DOI: 10.1186/s13073-023-01247-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 10/13/2023] [Indexed: 11/12/2023] Open
Abstract
BACKGROUND Malaria continues to be a major threat to global public health. Whole genome sequencing (WGS) of the underlying Plasmodium parasites has provided insights into the genomic epidemiology of malaria. Genome sequencing is rapidly gaining traction as a diagnostic and surveillance tool for clinical settings, where the profiling of co-infections, identification of imported malaria parasites, and detection of drug resistance are crucial for infection control and disease elimination. To support this informatically, we have developed the Malaria-Profiler tool, which rapidly (within minutes) predicts Plasmodium species, geographical source, and resistance to antimalarial drugs directly from WGS data. RESULTS The online and command line versions of Malaria-Profiler detect ~ 250 markers from genome sequences covering Plasmodium speciation, likely geographical source, and resistance to chloroquine, sulfadoxine-pyrimethamine (SP), and other anti-malarial drugs for P. falciparum, but also providing mutations for orthologous resistance genes in other species. The predictive performance of the mutation library was assessed using 9321 clinical isolates with WGS and geographical data, with most being single-species infections (P. falciparum 7152/7462, P. vivax 1502/1661, P. knowlesi 143/151, P. malariae 18/18, P. ovale ssp. 5/5), but co-infections were identified (456/9321; 4.8%). The accuracy of the predicted geographical profiles was high to both continental (96.1%) and regional levels (94.6%). For P. falciparum, markers were identified for resistance to chloroquine (49.2%; regional range: 24.5% to 100%), sulfadoxine (83.3%; 35.4- 90.5%), pyrimethamine (85.4%; 80.0-100%) and combined SP (77.4%). Markers associated with the partial resistance of artemisinin were found in WGS from isolates sourced from Southeast Asia (30.6%). CONCLUSIONS Malaria-Profiler is a user-friendly tool that can rapidly and accurately predict the geographical regional source and anti-malarial drug resistance profiles across large numbers of samples with WGS data. The software is flexible with modifiable bioinformatic pipelines. For example, it is possible to select the sequencing platform, display specific variants, and customise the format of outputs. With the increasing application of next-generation sequencing platforms on Plasmodium DNA, Malaria-Profiler has the potential to be integrated into point-of-care and surveillance settings, thereby assisting malaria control. Malaria-Profiler is available online (bioinformatics.lshtm.ac.uk/malaria-profiler) and as standalone software ( https://github.com/jodyphelan/malaria-profiler ).
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Affiliation(s)
- Jody E Phelan
- Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine (LSHTM), London, WC1E 7HT, UK.
| | - Anna Turkiewicz
- Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine (LSHTM), London, WC1E 7HT, UK
| | - Emilia Manko
- Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine (LSHTM), London, WC1E 7HT, UK
| | - Joseph Thorpe
- Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine (LSHTM), London, WC1E 7HT, UK
| | - Leen N Vanheer
- Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine (LSHTM), London, WC1E 7HT, UK
| | - Marga van de Vegte-Bolmer
- Department of Medical Microbiology and Radboud Center for Infectious Diseases, Radboud University Medical Center, University of Nijmegen, Nijmegen, The Netherlands
| | - Nguyen Thi Hong Ngoc
- Molecular Biology Department, Parasitology and Entomology, Vietnam National Institute of Malariology, Hanoi, Vietnam
| | - Nguyen Thi Huong Binh
- Molecular Biology Department, Parasitology and Entomology, Vietnam National Institute of Malariology, Hanoi, Vietnam
| | - Nguyen Quang Thieu
- Molecular Biology Department, Parasitology and Entomology, Vietnam National Institute of Malariology, Hanoi, Vietnam
| | - Jesse Gitaka
- Directorate of Research and Innovation, Mount Kenya University, Gen. Kago Rd, Thika, Kenya
| | - Debbie Nolder
- Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine (LSHTM), London, WC1E 7HT, UK
- UK Health Security Agency Malaria Reference Laboratory, LSHTM, London, WC1E 7HT, UK
| | - Khalid B Beshir
- Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine (LSHTM), London, WC1E 7HT, UK
| | - Jamille G Dombrowski
- Department of Parasitology, Institute of Biomedical Sciences, Univ. of São Paulo, São Paulo, Brazil
| | - Silvia Maria Di Santi
- School of Medicine, Instituto de Medicina Tropical, University of São Paulo, São Paulo, Brazil
| | - Teun Bousema
- Department of Medical Microbiology and Radboud Center for Infectious Diseases, Radboud University Medical Center, University of Nijmegen, Nijmegen, The Netherlands
| | - Colin J Sutherland
- Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine (LSHTM), London, WC1E 7HT, UK
- UK Health Security Agency Malaria Reference Laboratory, LSHTM, London, WC1E 7HT, UK
| | - Susana Campino
- Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine (LSHTM), London, WC1E 7HT, UK.
| | - Taane G Clark
- Faculty of Infectious and Tropical Diseases, London School of Hygiene & Tropical Medicine (LSHTM), London, WC1E 7HT, UK.
- Faculty of Epidemiology and Population Health, LSHTM, London, WC1E 7HT, UK.
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4
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Cecil RM, Sugden LA. On convolutional neural networks for selection inference: Revealing the effect of preprocessing on model learning and the capacity to discover novel patterns. PLoS Comput Biol 2023; 19:e1010979. [PMID: 38011281 PMCID: PMC10703409 DOI: 10.1371/journal.pcbi.1010979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 12/07/2023] [Accepted: 10/26/2023] [Indexed: 11/29/2023] Open
Abstract
A central challenge in population genetics is the detection of genomic footprints of selection. As machine learning tools including convolutional neural networks (CNNs) have become more sophisticated and applied more broadly, these provide a logical next step for increasing our power to learn and detect such patterns; indeed, CNNs trained on simulated genome sequences have recently been shown to be highly effective at this task. Unlike previous approaches, which rely upon human-crafted summary statistics, these methods are able to be applied directly to raw genomic data, allowing them to potentially learn new signatures that, if well-understood, could improve the current theory surrounding selective sweeps. Towards this end, we examine a representative CNN from the literature, paring it down to the minimal complexity needed to maintain comparable performance; this low-complexity CNN allows us to directly interpret the learned evolutionary signatures. We then validate these patterns in more complex models using metrics that evaluate feature importance. Our findings reveal that preprocessing steps, which determine how the population genetic data is presented to the model, play a central role in the learned prediction method. This results in models that mimic previously-defined summary statistics; in one case, the summary statistic itself achieves similarly high accuracy. For evolutionary processes that are less well understood than selective sweeps, we hope this provides an initial framework for using CNNs in ways that go beyond simply achieving high classification performance. Instead, we propose that CNNs might be useful as tools for learning novel patterns that can translate to easy-to-implement summary statistics available to a wider community of researchers.
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Affiliation(s)
- Ryan M. Cecil
- Department of Mathematics and Computer Science, Duquesne University, Pittsburgh, Pennsylvania, United States of America
- Department of Statistics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Lauren A. Sugden
- Department of Mathematics and Computer Science, Duquesne University, Pittsburgh, Pennsylvania, United States of America
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5
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Korfmann K, Gaggiotti OE, Fumagalli M. Deep Learning in Population Genetics. Genome Biol Evol 2023; 15:6997869. [PMID: 36683406 PMCID: PMC9897193 DOI: 10.1093/gbe/evad008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 12/19/2022] [Accepted: 01/16/2023] [Indexed: 01/24/2023] Open
Abstract
Population genetics is transitioning into a data-driven discipline thanks to the availability of large-scale genomic data and the need to study increasingly complex evolutionary scenarios. With likelihood and Bayesian approaches becoming either intractable or computationally unfeasible, machine learning, and in particular deep learning, algorithms are emerging as popular techniques for population genetic inferences. These approaches rely on algorithms that learn non-linear relationships between the input data and the model parameters being estimated through representation learning from training data sets. Deep learning algorithms currently employed in the field comprise discriminative and generative models with fully connected, convolutional, or recurrent layers. Additionally, a wide range of powerful simulators to generate training data under complex scenarios are now available. The application of deep learning to empirical data sets mostly replicates previous findings of demography reconstruction and signals of natural selection in model organisms. To showcase the feasibility of deep learning to tackle new challenges, we designed a branched architecture to detect signals of recent balancing selection from temporal haplotypic data, which exhibited good predictive performance on simulated data. Investigations on the interpretability of neural networks, their robustness to uncertain training data, and creative representation of population genetic data, will provide further opportunities for technological advancements in the field.
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Affiliation(s)
- Kevin Korfmann
- Professorship for Population Genetics, Department of Life Science Systems, Technical University of Munich, Germany
| | - Oscar E Gaggiotti
- Centre for Biological Diversity, Sir Harold Mitchell Building, University of St Andrews, Fife KY16 9TF, UK
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6
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Deelder W, Manko E, Phelan JE, Campino S, Palla L, Clark TG. Geographical classification of malaria parasites through applying machine learning to whole genome sequence data. Sci Rep 2022; 12:21150. [PMID: 36476815 PMCID: PMC9729610 DOI: 10.1038/s41598-022-25568-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 12/01/2022] [Indexed: 12/12/2022] Open
Abstract
Malaria, caused by Plasmodium parasites, is a major global health challenge. Whole genome sequencing (WGS) of Plasmodium falciparum and Plasmodium vivax genomes is providing insights into parasite genetic diversity, transmission patterns, and can inform decision making for clinical and surveillance purposes. Advances in sequencing technologies are helping to generate timely and big genomic datasets, with the prospect of applying Artificial Intelligence analytical techniques (e.g., machine learning) to support programmatic malaria control and elimination. Here, we assess the potential of applying deep learning convolutional neural network approaches to predict the geographic origin of infections (continents, countries, GPS locations) using WGS data of P. falciparum (n = 5957; 27 countries) and P. vivax (n = 659; 13 countries) isolates. Using identified high-quality genome-wide single nucleotide polymorphisms (SNPs) (P. falciparum: 750 k, P. vivax: 588 k), an analysis of population structure and ancestry revealed clustering at the country-level. When predicting locations for both species, classification (compared to regression) methods had the lowest distance errors, and > 90% accuracy at a country level. Our work demonstrates the utility of machine learning approaches for geo-classification of malaria parasites. With timelier WGS data generation across more malaria-affected regions, the performance of machine learning approaches for geo-classification will improve, thereby supporting disease control activities.
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Affiliation(s)
- Wouter Deelder
- London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
- Dalberg Advisors, 7 Rue de Chantepoulet, 1201, Geneva, Switzerland
| | - Emilia Manko
- London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Jody E Phelan
- London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Susana Campino
- London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
| | - Luigi Palla
- London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK
- Department of Public Health and Infectious Diseases, University of Rome La Sapienza, Rome, Italy
| | - Taane G Clark
- London School of Hygiene & Tropical Medicine, Keppel Street, London, WC1E 7HT, UK.
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7
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Sanchez T, Bray EM, Jobic P, Guez J, Letournel AC, Charpiat G, Cury J, Jay F. dnadna: a deep learning framework for population genetics inference. Bioinformatics 2022; 39:6851140. [PMID: 36445000 PMCID: PMC9825738 DOI: 10.1093/bioinformatics/btac765] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 10/30/2022] [Accepted: 11/28/2022] [Indexed: 11/30/2022] Open
Abstract
MOTIVATION We present dnadna, a flexible python-based software for deep learning inference in population genetics. It is task-agnostic and aims at facilitating the development, reproducibility, dissemination and re-usability of neural networks designed for population genetic data. RESULTS dnadna defines multiple user-friendly workflows. First, users can implement new architectures and tasks, while benefiting from dnadna utility functions, training procedure and test environment, which saves time and decreases the likelihood of bugs. Second, the implemented networks can be re-optimized based on user-specified training sets and/or tasks. Newly implemented architectures and pre-trained networks are easily shareable with the community for further benchmarking or other applications. Finally, users can apply pre-trained networks in order to predict evolutionary history from alternative real or simulated genetic datasets, without requiring extensive knowledge in deep learning or coding in general. dnadna comes with a peer-reviewed, exchangeable neural network, allowing demographic inference from SNP data, that can be used directly or retrained to solve other tasks. Toy networks are also available to ease the exploration of the software, and we expect that the range of available architectures will keep expanding thanks to community contributions. AVAILABILITY AND IMPLEMENTATION dnadna is a Python (≥3.7) package, its repository is available at gitlab.com/mlgenetics/dnadna and its associated documentation at mlgenetics.gitlab.io/dnadna/.
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Affiliation(s)
| | | | - Pierre Jobic
- Université Paris-Saclay, CNRS UMR 9015, INRIA, Laboratoire Interdisciplinaire des Sciences du Numérique, 91400 Orsay, France
- ENS Paris-Saclay, 91190 Gif-sur-Yvette, France
| | - Jérémy Guez
- Université Paris-Saclay, CNRS UMR 9015, INRIA, Laboratoire Interdisciplinaire des Sciences du Numérique, 91400 Orsay, France
- UMR7206 Eco-Anthropologie, Muséum National d’Histoire Naturelle, CNRS, Université de Paris, 75016 Paris, France
| | - Anne-Catherine Letournel
- Université Paris-Saclay, CNRS UMR 9015, INRIA, Laboratoire Interdisciplinaire des Sciences du Numérique, 91400 Orsay, France
| | - Guillaume Charpiat
- Université Paris-Saclay, CNRS UMR 9015, INRIA, Laboratoire Interdisciplinaire des Sciences du Numérique, 91400 Orsay, France
| | - Jean Cury
- To whom correspondence should be addressed. or
| | - Flora Jay
- To whom correspondence should be addressed. or
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8
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Hayat M, Tahir M, Alarfaj FK, Alturki R, Gazzawe F. NLP-BCH-Ens: NLP-based intelligent computational model for discrimination of malaria parasite. Comput Biol Med 2022; 149:105962. [DOI: 10.1016/j.compbiomed.2022.105962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Revised: 07/29/2022] [Accepted: 08/13/2022] [Indexed: 11/03/2022]
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9
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Nikolakis ZL, Adams RH, Wade KJ, Lund AJ, Carlton EJ, Castoe TA, Pollock DD. Prospects for genomic surveillance for selection in schistosome parasites. FRONTIERS IN EPIDEMIOLOGY 2022; 2:932021. [PMID: 38455290 PMCID: PMC10910990 DOI: 10.3389/fepid.2022.932021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 09/12/2022] [Indexed: 03/09/2024]
Abstract
Schistosomiasis is a neglected tropical disease caused by multiple parasitic Schistosoma species, and which impacts over 200 million people globally, mainly in low- and middle-income countries. Genomic surveillance to detect evidence for natural selection in schistosome populations represents an emerging and promising approach to identify and interpret schistosome responses to ongoing control efforts or other environmental factors. Here we review how genomic variation is used to detect selection, how these approaches have been applied to schistosomes, and how future studies to detect selection may be improved. We discuss the theory of genomic analyses to detect selection, identify experimental designs for such analyses, and review studies that have applied these approaches to schistosomes. We then consider the biological characteristics of schistosomes that are expected to respond to selection, particularly those that may be impacted by control programs. Examples include drug resistance, host specificity, and life history traits, and we review our current understanding of specific genes that underlie them in schistosomes. We also discuss how inherent features of schistosome reproduction and demography pose substantial challenges for effective identification of these traits and their genomic bases. We conclude by discussing how genomic surveillance for selection should be designed to improve understanding of schistosome biology, and how the parasite changes in response to selection.
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Affiliation(s)
- Zachary L. Nikolakis
- Department of Biology, University of Texas at Arlington, Arlington, TX, United States
| | - Richard H. Adams
- Department of Biological and Environmental Sciences, Georgia College and State University, Milledgeville, GA, United States
| | - Kristen J. Wade
- Department of Biochemistry and Molecular Genetics, University of Colorado School of Medicine, Aurora, CO, United States
| | - Andrea J. Lund
- Department of Environmental and Occupational Health, Colorado School of Public Health, University of Colorado, Anschutz, Aurora, CO, United States
| | - Elizabeth J. Carlton
- Department of Environmental and Occupational Health, Colorado School of Public Health, University of Colorado, Anschutz, Aurora, CO, United States
| | - Todd A. Castoe
- Department of Biology, University of Texas at Arlington, Arlington, TX, United States
| | - David D. Pollock
- Department of Biochemistry and Molecular Genetics, University of Colorado School of Medicine, Aurora, CO, United States
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10
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Amin J, Sharif M, Mallah GA, Fernandes SL. An optimized features selection approach based on Manta Ray Foraging Optimization (MRFO) method for parasite malaria classification. Front Public Health 2022; 10:969268. [PMID: 36148344 PMCID: PMC9486170 DOI: 10.3389/fpubh.2022.969268] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 08/03/2022] [Indexed: 01/25/2023] Open
Abstract
Malaria is a serious and lethal disease that has been reported by the World Health Organization (WHO), with an estimated 219 million new cases and 435,000 deaths globally. The most frequent malaria detection method relies mainly on the specialists who examine the samples under a microscope. Therefore, a computerized malaria diagnosis system is required. In this article, malaria cell segmentation and classification methods are proposed. The malaria cells are segmented using a color-based k-mean clustering approach on the selected number of clusters. After segmentation, deep features are extracted using pre-trained models such as efficient-net-b0 and shuffle-net, and the best features are selected using the Manta-Ray Foraging Optimization (MRFO) method. Two experiments are performed for classification using 10-fold cross-validation, the first experiment is based on the best features selected from the pre-trained models individually, while the second experiment is performed based on the selection of best features from the fusion of extracted features using both pre-trained models. The proposed method provided an accuracy of 99.2% for classification using the linear kernel of the SVM classifier. An empirical study demonstrates that the fused features vector results are better as compared to the individual best-selected features vector and the existing latest methods published so far.
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Affiliation(s)
- Javeria Amin
- Department of Computer Science, University of Wah, Wah Cantt, Pakistan,*Correspondence: Javeria Amin
| | - Muhammad Sharif
- Department of Computer Science, COMSATS University Islamabad, Islamabad, Pakistan
| | - Ghulam Ali Mallah
- Department of Computer Science, Shah Abdul Latif University, Khairpur, Pakistan
| | - Steven L. Fernandes
- Department of Computer Science, Design and Journalism, Creighton University, Omaha, NE, United States
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11
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Dutta AK, Mageswari RU, Gayathri A, Dallfin Bruxella JM, Ishak MK, Mostafa SM, Hamam H. Barnacles Mating Optimizer with Deep Transfer Learning Enabled Biomedical Malaria Parasite Detection and Classification. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:7776319. [PMID: 35694571 PMCID: PMC9177294 DOI: 10.1155/2022/7776319] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 04/29/2022] [Accepted: 05/07/2022] [Indexed: 01/06/2023]
Abstract
Biomedical engineering involves ideologies and problem-solving methods of engineering to biology and medicine. Malaria is a life-threatening illness, which has gained significant attention among researchers. Since the manual diagnosis of malaria in a clinical setting is tedious, automated tools based on computational intelligence (CI) tools have gained considerable interest. Though earlier studies were focused on the handcrafted features, the diagnostic accuracy can be boosted through deep learning (DL) methods. This study introduces a new Barnacles Mating Optimizer with Deep Transfer Learning Enabled Biomedical Malaria Parasite Detection and Classification (BMODTL-BMPC) model. The presented BMODTL-BMPC model involves the design of intelligent models for the recognition and classification of malaria parasites. Initially, the Gaussian filtering (GF) approach is employed to eradicate noise in blood smear images. Then, Graph cuts (GC) segmentation technique is applied to determine the affected regions in the blood smear images. Moreover, the barnacles mating optimizer (BMO) algorithm with the NasNetLarge model is employed for the feature extraction process. Furthermore, the extreme learning machine (ELM) classification model is employed for the identification and classification of malaria parasites. To assure the enhanced outcomes of the BMODTL-BMPC technique, a wide-ranging experimentation analysis is performed using a benchmark dataset. The experimental results show that the BMODTL-BMPC technique outperforms other recent approaches.
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Affiliation(s)
- Ashit Kumar Dutta
- Department of Computer Science and Information System, College of Applied Sciences, AlMaarefa University, Riyadh 11597, Saudi Arabia
| | - R. Uma Mageswari
- Department of Computer Science and Engineering, Vardhaman College of Engineering (Autonomous), Hyderabad, Telangana, India
| | - A. Gayathri
- Department of Information Technology, School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India
| | - J. Mary Dallfin Bruxella
- Department of Computer Science and Information Technology, Kalasalingam Academy of Research and Education, Krishnankoil, India
| | - Mohamad Khairi Ishak
- School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Nibong Tebal 14300, Pulau Pinang, Malaysia
| | - Samih M. Mostafa
- Faculty of Computers and Information, South Valley University, Egypt
| | - Habib Hamam
- Faculty of Engineering, Université de Moncton, Moncton, NB E1A 3E9, Canada
- School of Electrical Engineering, Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South Africa
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