Automating Bird Detection Based on Webcam Captured Images using Deep Learning

Mirugwe, Alex; Nyirenda, Juwa; Dufourq, Emmanuel

doi:10.29007/9fr5

Cited by 4 publications

(3 citation statements)

References 36 publications

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“…A recent paper [3] explaining the deep learning architecture for the detection of birds from the images captured by the webcam was published. For the preprocessing of data, deep learning algorithm's capability for the detection of birds inside the images is checked first.…”

Section: Literature Reviewmentioning

confidence: 99%

Bird Image Classification using Convolutional Neural Network Transfer Learning Architectures

Manna¹,

Upasani²,

Jadhav³

et al. 2023

IJACSA

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With the technological progress of human beings, more and more animal and bird species are being endangered and sometimes even going to the verge of extinction. However, the existence of birds is highly beneficial for human civilization as birds help in pollination, destroying harmful insects for crops, etc. To ensure the healthy co-existence of all species along with human beings, almost all advanced countries have taken up some conservation measures for endangered species. To ensure conservation, the first step is to identify the species of birds found in different locations. Deep learning-based techniques are best suited for the automated identification of bird species from the captured images. In this paper, a Convolutional Neural Network based bird image identification methodology has been proposed. Four different transfer learning-based architectures, namely Resnet152V2, Inception V3, Densenet201, and MobileNetV2 have been used for bird image classification and identification. The models have been trained using 58388 images belonging to 400 species of birds, and the models have been tested using 2000 images belonging to 400 species of birds. Out of these four models, Resnet152V2 and DenseNet201 performed comparatively well. The accuracy of Resnet152V2 was highest at 95.45%, but it faced a large loss of 0.8835. But based on the results, even though DenseNet201 had an accuracy of 95.05%, it faced less loss i.e., of 0.6854. The results show that the DenseNet201 model can further be used for real-life bird image classification.

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Section: Literature Reviewmentioning

confidence: 99%

Bird Image Classification using Convolutional Neural Network Transfer Learning Architectures

Manna¹,

Upasani²,

Jadhav³

et al. 2023

IJACSA

View full text Add to dashboard Cite

show abstract

“…This weighted sum is then passed through the neuron's activation function to produce an output. The purpose of the fully connected layer is to learn complex relationships between the inputs and produce a final output, which can be used for either classification or regression (Mirugwe et al, 2022).…”

Section: ) Pooling Layermentioning

confidence: 99%

Cervical Cancer Screening: Artificial Intelligence Algorithm For Automatic Diagnostic Support

Mirugwe,

Ashaba

2023

Preprint

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Introduction: Cervical cancer is the fourth most prevalent cancer among women worldwide and is a significant contributor to cancer-related deaths, with an estimated 300,000 women losing their lives to the disease annually. Most of these fatalities occur in low and middle- income countries (LMICs), such as Uganda, where access to screening and treatment options is limited. Early detection of cervical cancer is crucial to improve the chances of survival for patients. Currently, cervical cancer screening is typically performed through Pap smears, which involve manual examination of cervical samples for abnormalities by medical experts. This process is costly, time-consuming, and prone to errors, leading to inaccurate diagnoses. Therefore, it is essential to find more effective and efficient alternative methods for cervical cancer screening to improve access in LMICs and alleviate the burden of cervical cancer. Objective: The purpose of this study is to develop an automated pre-cervical cancer screening algorithm to detect precancerous cervical lesions. Methodology: We developed a cancer screening algorithm using a 21-layer deep-learning convolution neural network trained on a dataset of 2300 images collected from local sources and some obtained from Kaggle. Results: The best-performing classifier had an AUC of the accuracy of 91.37%, a precision of 88.80%, a recall of 94.69%, an F1 score of 91.65%, and an AUC of 96.0%. Conclusion: The development and implementation of automated pre-cervical cancer screening algorithms have the potential to revolutionize cervical cancer detection and contribute significantly to reducing the burden of the disease, particularly in resource-limited settings.

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“…(EHRs), pharmaceutical developments, and public health informatics. This surge has mainly been attributed to the availability of vast amounts of data, an increase in the computation power, increased demand for more efficient technologies, and the development of novel state-of-the-art AI methods [13].…”

mentioning

confidence: 99%

Adoption of Artificial Intelligence in the Ugandan Health Sector: a review of Literature

Mirugwe

2023

Preprint

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This paper presents a comprehensive literature review on the application of artificial intelligence techniques in Ugandan healthcare and the medical industry. Recently, the data generated in the health domain has exceeded the human cognitive capacity to analyze it effectively. Several approaches have been suggested to address this problem but in several studies, Artificial Intelligence has been found to be the best and the most effective solution as far as speed, accuracy, robustness, and reliability are concerned. We searched and reviewed AI health-related peer-reviewed articles in ScienceDirect, Springer, PubMed, arXiv, IEEE Xplore, medRxiv, PLOS, Wiley Online Library, BioMed Central, bioRxiv, and Scopus published between 2012 and 2022. This literature survey covered 38 research papers, and the review showed that the most applied AI subfields are statistical learning, machine learning, and deep learning. The paper highlights the challenges, gaps, and opportunities required to improve and advance the application of AI in the Ugandan healthcare industry. We believe this study will help researchers and policymakers to foster AI innovations better.

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Automating Bird Detection Based on Webcam Captured Images using Deep Learning

Cited by 4 publications

References 36 publications

Bird Image Classification using Convolutional Neural Network Transfer Learning Architectures

Bird Image Classification using Convolutional Neural Network Transfer Learning Architectures

Cervical Cancer Screening: Artificial Intelligence Algorithm For Automatic Diagnostic Support

Adoption of Artificial Intelligence in the Ugandan Health Sector: a review of Literature

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