Eye-Zheimer: A Deep Transfer Learning Approach of Dementia Detection and Classification from NeuroImaging

Alon, Helcy D.; Ligayo, Michael Angelo D.; Misola, Maribel A.; Sandoval, Allan A.; Fontanilla, Marites V.

doi:10.1109/icetas51660.2020.9484315

Cited by 8 publications

(3 citation statements)

References 23 publications

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“…Annotations are saved as XML files in PASCAL VOC format. Moreover, it also supports YOLO and CreateML formats [ 58 , 59 ]. During the labelling process, the area of object and its class belonging will be determined.…”

Section: Resultsmentioning

confidence: 99%

Deep Learning for Clothing Style Recognition Using YOLOv5

Chang

Zhang

2022

Micromachines

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With the rapid development of artificial intelligence, much more attention has been paid to deep learning. However, as the complexity of learning algorithms increases, the needs of computation power of hardware facilities become more crucial. Instead of the focus being on computing devices like GPU computers, a lightweight learning algorithm could be the answer for this problem. Cross-domain applications of deep learning have attracted great interest amongst researchers in academia and industries. For beginners who do not have enough support with software and hardware, an open-source development environment is very helpful. In this paper, a relatively lightweight algorithm YOLOv5s is addressed, and the Google Colab is used for model training and testing. Based on the developed environment, many state-of-art learning algorithms can be studied for performance comparisons. To highlight the benefits of one-stage object detection algorithms, the recognition of clothing styles is investigated. The image samples are selected from datasets of fashion clothes and the web crawling of online stores. The image data are categorized into five groups: plaid; plain; block; horizontal; and vertical. Average precison, mean average precison, recall, F1-score, model size, and frame per second are the metrics used for performance validations. From the experimental outcomes, it shows that YOLOv5s is better than other learning algorithms in the recognition accuracy and detection speed.

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Section: Resultsmentioning

confidence: 99%

Deep Learning for Clothing Style Recognition Using YOLOv5

Chang

Zhang

2022

Micromachines

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“…In a study conducted by Alon et al [36], the YOLOv3 architecture demonstrated an accuracy rate of 80%, which was notably the lowest among the studies under review. It's important to highlight that this study employed a significant dataset comprising 1000 MRI images for training and validation, achieving impressive results with training accuracy reaching 98.617%, validation accuracy at 98.8207%, and a mean average precision (mAP) of 96.17%.…”

Section: K Yolo Architechturementioning

confidence: 92%

Enhancing Alzheimer's Disease Diagnosis: The Efficacy of the YOLO Algorithm Model

Vinh,

Byeon

2023

IJACSA

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The diagnosis and early detection of Alzheimer's Disease (AD) and other forms of dementia have become increasingly crucial as our aging population grows. In recent years, deep learning, particularly the You Only Look Once (YOLO) architecture, has emerged as a promising tool in the field of neuroimaging and machine learning for AD diagnosis. This comprehensive review investigates the recent advances in the application of YOLO for AD diagnosis and classification. We scrutinized five research papers that have explored the potential of YOLO, delving into the methodologies, datasets, and results presented. Our review reveals the remarkable strides made in AD diagnosis using YOLO, while also highlighting challenges, such as data scarcity and research lacking. The paper provides insights into the growing role of YOLO in the early detection of AD and its potential to transform clinical practices in the field. This review aims to inspire further research and innovation to enhance AD diagnosis and, ultimately, patient care.

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“…A large amount of annotated data are needed when training the deep learning model. In this study, the worms in each image were annotated manually using the image annotation software LabelImg, which supports the Visual Object Classes (VOC) format [ 65 , 66 ]. This software can generate Extensible Markup Language (XML) files for the model.…”

Section: Methodsmentioning

confidence: 99%

Deep Learning for Microfluidic-Assisted Caenorhabditis elegans Multi-Parameter Identification Using YOLOv7

et al. 2023

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The Caenorhabditis elegans (C. elegans) is an ideal model organism for studying human diseases and genetics due to its transparency and suitability for optical imaging. However, manually sorting a large population of C. elegans for experiments is tedious and inefficient. The microfluidic-assisted C. elegans sorting chip is considered a promising platform to address this issue due to its automation and ease of operation. Nevertheless, automated C. elegans sorting with multiple parameters requires efficient identification technology due to the different research demands for worm phenotypes. To improve the efficiency and accuracy of multi-parameter sorting, we developed a deep learning model using You Only Look Once (YOLO)v7 to detect and recognize C. elegans automatically. We used a dataset of 3931 annotated worms in microfluidic chips from various studies. Our model showed higher precision in automated C. elegans identification than YOLOv5 and Faster R-CNN, achieving a mean average precision (mAP) at a 0.5 intersection over a union (mAP@0.5) threshold of 99.56%. Additionally, our model demonstrated good generalization ability, achieving an mAP@0.5 of 94.21% on an external validation set. Our model can efficiently and accurately identify and calculate multiple phenotypes of worms, including size, movement speed, and fluorescence. The multi-parameter identification model can improve sorting efficiency and potentially promote the development of automated and integrated microfluidic platforms.

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Eye-Zheimer: A Deep Transfer Learning Approach of Dementia Detection and Classification from NeuroImaging

Cited by 8 publications

References 23 publications

Deep Learning for Clothing Style Recognition Using YOLOv5

Deep Learning for Clothing Style Recognition Using YOLOv5

Enhancing Alzheimer's Disease Diagnosis: The Efficacy of the YOLO Algorithm Model

Deep Learning for Microfluidic-Assisted Caenorhabditis elegans Multi-Parameter Identification Using YOLOv7

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