Pneumonia X‐ray detection with anchor‐free detection framework and data augmentation

Yan, Nan; Ye, Tao

doi:10.1002/ima.22860

Cited by 2 publications

(1 citation statement)

References 47 publications

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“…Since only non-maximum suppression (NMS) is used for post-processing, the FCOS proposed in this paper has the advantage of being simpler than previous first-order detectors based on anchor frames. In addition, an enhanced FCOS (NYNet), which is proposed by Yan et al 33 , is adopted for comparisons. Specifically, for fair competition, the detection performance using ResNet50 is included in Table 3 .…”

Section: Resultsmentioning

confidence: 99%

Pneumonia detection based on RSNA dataset and anchor-free deep learning detector

Wu,

Zhang,

Wang

et al. 2024

Sci Rep

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Pneumonia is a highly lethal disease, and research on its treatment and early screening tools has received extensive attention from researchers. Due to the maturity and cost reduction of chest X-ray technology, and with the development of artificial intelligence technology, pneumonia identification based on deep learning and chest X-ray has attracted attention from all over the world. Although the feature extraction capability of deep learning is strong, existing deep learning object detection frameworks are based on pre-defined anchors, which require a lot of tuning and experience to guarantee their excellent results in the face of new applications or data. To avoid the influence of anchor settings in pneumonia detection, this paper proposes an anchor-free object detection framework and RSNA dataset based on pneumonia detection. First, a data enhancement scheme is used to preprocess the chest X-ray images; second, an anchor-free object detection framework is used for pneumonia detection, which contains a feature pyramid, two-branch detection head, and focal loss. The average precision of 51.5 obtained by Intersection over Union (IoU) calculation shows that the pneumonia detection results obtained in this paper can surpass the existing classical object detection framework, providing an idea for future research and exploration.

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Section: Resultsmentioning

confidence: 99%

Pneumonia detection based on RSNA dataset and anchor-free deep learning detector

Wu,

Zhang,

Wang

et al. 2024

Sci Rep

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Enhancing Pneumonia Segmentation in Lung Radiographs: A Jellyfish Search Optimizer Approach

Zarate,

Zaldívar,

Cuevas

et al. 2023

Mathematics

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Segmentation of pneumonia on lung radiographs is vital for the precise diagnosis and monitoring of the disease. It enables healthcare professionals to locate and quantify the extent of infection, guide treatment decisions, and improve patient care. One of the most-employed approaches to effectively segment pneumonia in lung radiographs is to treat it as an optimization task. By formulating the problem in this manner, it is possible to use the interesting capabilities of metaheuristic methods to determine the optimal segmentation solution. Although these methods produce interesting results, they frequently produce suboptimal solutions owing to the lack of exploration of the search space. In this paper, a new segmentation method for segmenting pneumonia in lung radiographs is introduced. The algorithm is based on the jellyfish search optimizer (JSO), which is characterized by its excellent global exploration capability and robustness. This method uses an energy curve based on cross-entropy as a cost function that penalizes misclassified pixels more heavily, leading to a sharper focus on regions where segmentation errors occur. This is particularly important because it allows for the accurate delineation of objects or regions of interest. To validate our proposed approach, we conducted extensive testing on the most widely available datasets. The results of our method were compared with those obtained using other established techniques. The results of our evaluation demonstrate that our approach consistently outperforms the other methods at levels 8, 16, and 32, with a difference of more than 10%.

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Pneumonia X‐ray detection with anchor‐free detection framework and data augmentation

Cited by 2 publications

References 47 publications

Pneumonia detection based on RSNA dataset and anchor-free deep learning detector

Pneumonia detection based on RSNA dataset and anchor-free deep learning detector

Enhancing Pneumonia Segmentation in Lung Radiographs: A Jellyfish Search Optimizer Approach

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