Gender identification of Drosophila melanogaster based on morphological analysis of microscopic images

Mestetskiy, Leonid M.; Guru, D. S.; Benifa, J. V. Bibal; Nagendraswamy, H. S.; Chola, Channabasava

doi:10.1007/s00371-022-02447-9

Cited by 8 publications

(2 citation statements)

References 36 publications

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“…For direct comparison using the same dataset, four deep learning models of DenseNet 201, ResNet50, Inception-V3, and Mobilenet-V2 are adopted and used. These AI models are selected to perform such direct comparison due to their promising classification performance in the research domain [ 21 , 37 , 47 , 58 , 59 , 64 , 65 ]. Such comparison is important to investigate the reliability of the proposed model with the trusted ones.…”

Section: Discussionmentioning

confidence: 99%

BCNet: A Deep Learning Computer-Aided Diagnosis Framework for Human Peripheral Blood Cell Identification

Chola

Muaad²,

Heyat³

et al. 2022

Diagnostics

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Blood cells carry important information that can be used to represent a person’s current state of health. The identification of different types of blood cells in a timely and precise manner is essential to cutting the infection risks that people face on a daily basis. The BCNet is an artificial intelligence (AI)-based deep learning (DL) framework that was proposed based on the capability of transfer learning with a convolutional neural network to rapidly and automatically identify the blood cells in an eight-class identification scenario: Basophil, Eosinophil, Erythroblast, Immature Granulocytes, Lymphocyte, Monocyte, Neutrophil, and Platelet. For the purpose of establishing the dependability and viability of BCNet, exhaustive experiments consisting of five-fold cross-validation tests are carried out. Using the transfer learning strategy, we conducted in-depth comprehensive experiments on the proposed BCNet’s architecture and test it with three optimizers of ADAM, RMSprop (RMSP), and stochastic gradient descent (SGD). Meanwhile, the performance of the proposed BCNet is directly compared using the same dataset with the state-of-the-art deep learning models of DensNet, ResNet, Inception, and MobileNet. When employing the different optimizers, the BCNet framework demonstrated better classification performance with ADAM and RMSP optimizers. The best evaluation performance was achieved using the RMSP optimizer in terms of 98.51% accuracy and 96.24% F1-score. Compared with the baseline model, the BCNet clearly improved the prediction accuracy performance 1.94%, 3.33%, and 1.65% using the optimizers of ADAM, RMSP, and SGD, respectively. The proposed BCNet model outperformed the AI models of DenseNet, ResNet, Inception, and MobileNet in terms of the testing time of a single blood cell image by 10.98, 4.26, 2.03, and 0.21 msec. In comparison to the most recent deep learning models, the BCNet model could be able to generate encouraging outcomes. It is essential for the advancement of healthcare facilities to have such a recognition rate improving the detection performance of the blood cells.

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Section: Discussionmentioning

confidence: 99%

BCNet: A Deep Learning Computer-Aided Diagnosis Framework for Human Peripheral Blood Cell Identification

Chola

Muaad²,

Heyat³

et al. 2022

Diagnostics

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“…Furthermore, the Resnet-based Single-Shot Detector framework model was used to identify faces. In other studies, Mestetskiy et al [36] proposed biomedical image classification and detection using image processing and computational geometry [37]. The DL-based face mask detection task plays an essential role in various scenarios such as public transport, workplace management, schools, and hospitals.…”

Section: Introductionmentioning

confidence: 99%

FMDNet: An Efficient System for Face Mask Detection Based on Lightweight Model during COVID-19 Pandemic in Public Areas

Benifa

Chola

Muaad

et al. 2023

Sensors

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A new artificial intelligence-based approach is proposed by developing a deep learning (DL) model for identifying the people who violate the face mask protocol in public places. To achieve this goal, a private dataset was created, including different face images with and without masks. The proposed model was trained to detect face masks from real-time surveillance videos. The proposed face mask detection (FMDNet) model achieved a promising detection of 99.0% in terms of accuracy for identifying violations (no face mask) in public places. The model presented a better detection capability compared to other recent DL models such as FSA-Net, MobileNet V2, and ResNet by 24.03%, 5.0%, and 24.10%, respectively. Meanwhile, the model is lightweight and had a confidence score of 99.0% in a resource-constrained environment. The model can perform the detection task in real-time environments at 41.72 frames per second (FPS). Thus, the developed model can be applicable and useful for governments to maintain the rules of the SOP protocol.

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A Deep Learning Model for Human Blood Cells Classification

Pramodha

Benifa

Al-Sarem

et al. 2023

Lecture Notes on Data Engineering and Communications Technologies

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Gender identification of Drosophila melanogaster based on morphological analysis of microscopic images

Cited by 8 publications

References 36 publications

BCNet: A Deep Learning Computer-Aided Diagnosis Framework for Human Peripheral Blood Cell Identification

BCNet: A Deep Learning Computer-Aided Diagnosis Framework for Human Peripheral Blood Cell Identification

FMDNet: An Efficient System for Face Mask Detection Based on Lightweight Model during COVID-19 Pandemic in Public Areas

A Deep Learning Model for Human Blood Cells Classification

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