Focus stacking technique in identification of forensically important Chrysomya species (Diptera: Calliphoridae)

Elleboudy, Noha A.; Eldin, Hayam Mohamed Ezz; Azab, Sonya M.S.

doi:10.1016/j.ejfs.2016.06.001

Cited by 8 publications

(5 citation statements)

References 7 publications

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“…Here, parameters such as opacity, contrast, brightness, and combining ratio of the two images are important. The more accurately these ratios, the more amount of noise is reduced in the images, and the higher the quality ratio [32].…”

Section: Reconstructing Images: Stacking Techniquementioning

confidence: 99%

COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches

Toğaçar

Ergen

Cömert

2020

Computers in Biology and Medicine

470

357

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Coronavirus causes a wide variety of respiratory infections and it is an RNA-type virus that can infect both humans and animal species. It often causes pneumonia in humans. Artificial intelligence models have been helpful for successful analyses in the biomedical field. In this study, Coronavirus was detected using a deep learning model, which is a sub-branch of artificial intelligence. Our dataset consists of three classes namely: coronavirus, pneumonia, and normal X-ray imagery. In this study, the data classes were restructured using the Fuzzy Color technique as a preprocessing step and the images that were structured with the original images were stacked. In the next step, the stacked dataset was trained with deep learning models (MobileNetV2, SqueezeNet) and the feature sets obtained by the models were processed using the Social Mimic optimization method. Thereafter, efficient features were combined and classified using Support Vector Machines (SVM). The overall classification rate obtained with the proposed approach was 99.27%. With the proposed approach in this study, it is evident that the model can efficiently contribute to the detection of COVID-19 disease.

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Section: Reconstructing Images: Stacking Techniquementioning

confidence: 99%

COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches

Toğaçar

Ergen

Cömert

2020

Computers in Biology and Medicine

470

357

View full text Add to dashboard Cite

show abstract

“…Firstly, some loss of potentially informative species-specific variation is probably inevitable with the image binarisation step. For example, wing membrane pigmentation is potentially informative for discriminating some species, such as Chrysomya marginalis, species from the Isomyia, Stomorhina [37] and Hypopygiopsis genera [38]. Secondly, the generalisability of the model's performance for identification of female flies is unknown since it was trained using only male specimens.…”

Section: Discussionmentioning

confidence: 99%

Accurate Identification of Fly Species in Forensic, Medical and Veterinary Entomology using Wing Venation Patterns

Ling

Ivorra

Heo

et al. 2023

Preprint

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In forensic, medical and veterinary entomology, the ease and affordability of image data acquisition have resulted in whole-image analysis becoming an invaluable approach for species identification. Krawtchouk moment invariants are a classical mathematical transformation that can extract local features from an image, thus allowing subtle species-specific biological variations to be accentuated for subsequent analyses. We extracted Krawtchouk moment invariant features from binarised wing images of 759 male fly specimens from the Calliphoridae, Sarcophagidae, and Muscidae families (13 species and a species variant). Subsequently, we trained the Generalized, Unbiased, Interaction Detection and Estimation (GUIDE) random forests classifier using linear discriminants derived from these features and inferred the species identity of specimens from the test samples. Five-fold cross validation results show a 98.56 ± 0.38% (standard error) identification accuracy at the family level, and a 91.04 ± 1.33% identification accuracy at the species level. The mean F1-score of 0.89 ± 0.02 reflects good balance of precision and recall properties of the model. The present study consolidates findings from previous small pilot studies of the usefulness of wing venation patterns for inferring species identities. Thus, the stage is set for the development of a mature data analytic ecosystem for routine computer image-based identification of fly species that are of forensic, medical, and veterinary importance.

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“…Firstly, some loss of potentially informative species‐specific variation is probably inevitable with the image binarisation step. For example, wing membrane pigmentation is potentially informative for discriminating some species, such as Chrysomya marginalis , species from the Isomyia , Stomorhina (Elleboudy et al, 2016) and Hypopygiopsis genera (Heo et al, 2015; Kurahashi et al, 1997). Secondly, the generalisability of the model's performance for identification of female flies is unknown since it was trained using only male specimens.…”

Section: Discussionmentioning

confidence: 99%

Machine learning analysis of wing venation patterns accurately identifies Sarcophagidae, Calliphoridae and Muscidae fly species

Ling

Ivorra

Heo

et al. 2023

Medical Vet Entomology

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In medical, veterinary and forensic entomology, the ease and affordability of image data acquisition have resulted in whole‐image analysis becoming an invaluable approach for species identification. Krawtchouk moment invariants are a classical mathematical transformation that can extract local features from an image, thus allowing subtle species‐specific biological variations to be accentuated for subsequent analyses. We extracted Krawtchouk moment invariant features from binarised wing images of 759 male fly specimens from the Calliphoridae, Sarcophagidae and Muscidae families (13 species and a species variant). Subsequently, we trained the Generalized, Unbiased, Interaction Detection and Estimation random forests classifier using linear discriminants derived from these features and inferred the species identity of specimens from the test samples. Fivefold cross‐validation results show a 98.56 ± 0.38% (standard error) mean identification accuracy at the family level and a 91.04 ± 1.33% mean identification accuracy at the species level. The mean F1‐score of 0.89 ± 0.02 reflects good balance of precision and recall properties of the model. The present study consolidates findings from previous small pilot studies of the usefulness of wing venation patterns for inferring species identities. Thus, the stage is set for the development of a mature data analytic ecosystem for routine computer image‐based identification of fly species that are of medical, veterinary and forensic importance.

show abstract

Focus stacking technique in identification of forensically important Chrysomya species (Diptera: Calliphoridae)

Cited by 8 publications

References 7 publications

COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches

COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches

Accurate Identification of Fly Species in Forensic, Medical and Veterinary Entomology using Wing Venation Patterns

Machine learning analysis of wing venation patterns accurately identifies Sarcophagidae, Calliphoridae and Muscidae fly species

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