COVID-19 localization and recognition on chest radiographs based on Yolov5 and EfficientNet

Zhang, Tong; Zhang, Boxuan; Zhao, FeiYun; Zhang, Shiran

doi:10.1109/icsp54964.2022.9778327

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…These models are very different from other models in these categories. Moreover, YOLOv5 exhibits better accuracy and speed than other versions of YOLOv4 and YOLOv3 (Zhang et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

“…For the environment, it is believed that the path can have 3 major movements: a straight path, a left path, and a right path. We tested this environment first with the YOLOv5 model (Zhang et al, 2022) and then compared it with the SSD algorithm. YOLOv5 produces better results with an average accuracy of 82.34% whereas SSD gives an average accuracy of 80%.…”

Section: Path Traversed Modulementioning

confidence: 99%

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A Deep Learning Based Model to Assist Blind People in Their Navigation

Kumar

Jain

2022

JITE:IIP

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Aim/Purpose: This paper proposes a new approach to developing a deep learning-based prototyping wearable model which can assist blind and visually disabled people to recognize their environments and navigate through them. As a result, visually impaired people will be able to manage day-to-day activities and navigate through the world around them more easily. Background: In recent decades, the development of navigational devices has posed challenges for researchers to design smart guidance systems for visually impaired and blind individuals in navigating through known or unknown environments. Efforts need to be made to analyze the existing research from a historical perspective. Early studies of electronic travel aids should be integrated with the use of assistive technology-based artificial vision models for visually impaired persons. Methodology: This paper is an advancement of our previous research work, where we performed a sensor-based navigation system. In this research, the navigation of the visually disabled person is carried out with a vision-based 3D-designed wearable model and a vision-based smart stick. The wearable model used a neural network-based You Only Look Once (YOLO) algorithm to detect the course of the navigational path which is augmented by a GPS-based smart Stick. Over 100 images of each of the three classes, namely straight path, left path and right path, are being trained using supervised learning. The model accurately predicts a straight path with 79% mean average precision (mAP), the right path with 83% mAP, and the left path with 85% mAP. The average accuracy of the wearable model is 82.33% and that of the smart stick is 96.14% which combined gives an overall accuracy of 89.24%. Contribution: This research contributes to the design of a low-cost navigational standalone system that will be handy to use and help people to navigate safely in real-time scenarios. The challenging self-built dataset of various paths is generated and transfer learning is performed on the YOLO-v5 model after augmentation and manual annotation. To analyze and evaluate the model, various metrics, such as model losses, recall value, precision, and maP, are used. Findings: These were the main findings of the study: • To detect objects, the deep learning model uses a higher version of YOLO, i.e., a YOLOv5 detector, that may help those with visual im-pairments to improve their quality of navigational mobilities in known or unknown environments. • The developed standalone model has an option to be integrated into any other assistive applications like Electronic Travel Aids (ETAs) • It is the single neural network technology that allows the model to achieve high levels of detection accuracy of around 0.823 mAP with a custom dataset as compared to 0.895 with the COCO dataset. Due to its lightning-speed of 45 FPS object detection technology, it has become popular. Recommendations for Practitioners: Practitioners can help the model’s efficiency by increasing the sample size and classes used in training the model. Recommendation for Researchers: To detect objects in an image or live cam, there are various algorithms, e.g., R-CNN, Retina Net, Single Shot Detector (SSD), YOLO. Researchers can choose to use the YOLO version owing to its superior performance. Moreover, one of the YOLO versions, YOLOv5, outperforms its other versions such as YOLOv3 and YOLOv4 in terms of speed and accuracy. Impact on Society: We discuss new low-cost technologies that enable visually impaired people to navigate effectively in indoor environments. Future Research: The future of deep learning could incorporate recurrent neural networks on a larger set of data with special AI-based processors to avoid latency.

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“…These models are very different from other models in these categories. Moreover, YOLOv5 exhibits better accuracy and speed than other versions of YOLOv4 and YOLOv3 (Zhang et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

Section: Path Traversed Modulementioning

confidence: 99%

A Deep Learning Based Model to Assist Blind People in Their Navigation

Kumar

Jain

2022

JITE:IIP

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“…In addition to the approaches of the SIIM-FISABIO-RSNA COVID-19 Detection Challenge, in [ 20 ] authors developed an ensemble of YoloV5 and EfficientNet to locate ground glass opacities from radiological images also obtained from the SIIM-FISABIO-RSNA COVID-19 Detection data set. No image pre-processing was used, and mosaic data enhancement was used to train the YoloV5 model.…”

Section: Introductionmentioning

confidence: 99%

Automatic Identification of Lung Opacities Due to COVID-19 from Chest X-ray Images—Focussing Attention on the Lungs

2023

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Due to the primary affection of the respiratory system, COVID-19 leaves traces that are visible in plain chest X-ray images. This is why this imaging technique is typically used in the clinic for an initial evaluation of the patient’s degree of affection. However, individually studying every patient’s radiograph is time-consuming and requires highly skilled personnel. This is why automatic decision support systems capable of identifying those lesions due to COVID-19 are of practical interest, not only for alleviating the workload in the clinic environment but also for potentially detecting non-evident lung lesions. This article proposes an alternative approach to identify lung lesions associated with COVID-19 from plain chest X-ray images using deep learning techniques. The novelty of the method is based on an alternative pre-processing of the images that focuses attention on a certain region of interest by cropping the original image to the area of the lungs. The process simplifies training by removing irrelevant information, improving model precision, and making the decision more understandable. Using the FISABIO-RSNA COVID-19 Detection open data set, results report that the opacities due to COVID-19 can be detected with a Mean Average Precision with an IoU > 0.5 (mAP@50) of 0.59 following a semi-supervised training procedure and an ensemble of two architectures: RetinaNet and Cascade R-CNN. The results also suggest that cropping to the rectangular area occupied by the lungs improves the detection of existing lesions. A main methodological conclusion is also presented, suggesting the need to resize the available bounding boxes used to delineate the opacities. This process removes inaccuracies during the labelling procedure, leading to more accurate results. This procedure can be easily performed automatically after the cropping stage.

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Automated Recipe Generation using Ingredient Classification based on an Image from a Real-Time Photo Station

Kaushik,

Srinivas

et al. 2023

2023 4th International Conference for Emerging Technology (INCET)

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COVID-19 localization and recognition on chest radiographs based on Yolov5 and EfficientNet

Cited by 3 publications

References 9 publications

A Deep Learning Based Model to Assist Blind People in Their Navigation

A Deep Learning Based Model to Assist Blind People in Their Navigation

Automatic Identification of Lung Opacities Due to COVID-19 from Chest X-ray Images—Focussing Attention on the Lungs

Automated Recipe Generation using Ingredient Classification based on an Image from a Real-Time Photo Station

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