Cervical cancer classification based on a bilinear convolutional neural network approach and random projection

Abd-Alhalem, Samia M.; Marie, Hanaa Salem; El-Shafai, Walid; Altameem, Torki; Rathore, Rajkumar Singh; Hassan, Tarek M.

doi:10.1016/j.engappai.2023.107261

Cited by 7 publications

(1 citation statement)

References 32 publications

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“…Deep learning technology plays an important role in processing medical images nowadays [10][11][12][14][15][16][17][18]. In medical image segmentation tasks, U-shape network architectures [19][20][21][22][23][24] are the most commonly used in medical image segmentation and have achieved top-ranking accuracy in many tasks.…”

Section: Introductionmentioning

confidence: 99%

Inferior Alveolar Nerve Canal Segmentation on CBCT Using U-Net with Frequency Attentions

Liu,

Yang,

Zhang

et al. 2024

Bioengineering

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Accurate inferior alveolar nerve (IAN) canal segmentation has been considered a crucial task in dentistry. Failing to accurately identify the position of the IAN canal may lead to nerve injury during dental procedures. While IAN canals can be detected from dental cone beam computed tomography, they are usually difficult for dentists to precisely identify as the canals are thin, small, and span across many slices. This paper focuses on improving accuracy in segmenting the IAN canals. By integrating our proposed frequency-domain attention mechanism in UNet, the proposed frequency attention UNet (FAUNet) is able to achieve 75.55% and 81.35% in the Dice and surface Dice coefficients, respectively, which are much higher than other competitive methods, by adding only 224 parameters to the classical UNet. Compared to the classical UNet, our proposed FAUNet achieves a 2.39% and 2.82% gain in the Dice coefficient and the surface Dice coefficient, respectively. The potential advantage of developing attention in the frequency domain is also discussed, which revealed that the frequency-domain attention mechanisms can achieve better performance than their spatial-domain counterparts.

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Section: Introductionmentioning

confidence: 99%

Inferior Alveolar Nerve Canal Segmentation on CBCT Using U-Net with Frequency Attentions

Liu,

Yang,

Zhang

et al. 2024

Bioengineering

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Differential evolution optimization based ensemble framework for accurate cervical cancer diagnosis

Bilal,

Asif,

Zhao

et al. 2024

Applied Soft Computing

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Demystifying COVID-19 mortality causes with interpretable data mining

Qian,

Zuo,

et al. 2024

Sci Rep

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While COVID-19 becomes periodical, old individuals remain vulnerable to severe disease with high mortality. Although there have been some studies on revealing different risk factors affecting the death of COVID-19 patients, researchers rarely provide a comprehensive analysis to reveal the relationships and interactive effects of the risk factors of COVID-19 mortality, especially in the elderly. Through retrospectively including 1917 COVID-19 patients (102 were dead) admitted to Xiangya Hospital from December 2022 to March 2023, we used the association rule mining method to identify the risk factors leading causes of death among the elderly. Firstly, we used the Affinity Propagation clustering to extract key features from the dataset. Then, we applied the Apriori Algorithm to obtain 6 groups of abnormal feature combinations with significant increments in mortality rate. The results showed a relationship between the number of abnormal feature combinations and mortality rates within different groups. Patients with “C-reactive protein > 8 mg/L”, “neutrophils percentage > 75.0 %”, “lymphocytes percentage < 20%”, and “albumin < 40 g/L” have a 2$$\times$$ × mortality rate than the basic one. When the characteristics of “D-dimer > 0.5 mg/L” and “WBC > $$9.5 \times 10^9$$ 9.5 × 10 9 /L” are continuously included in this foundation, the mortality rate can be increased to 3$$\times$$ × or 4$$\times$$ × . In addition, we also found that liver and kidney diseases significantly affect patient mortality, and the mortality rate can be as high as 100%. These findings can support auxiliary diagnosis and treatment to facilitate early intervention in patients, thereby reducing patient mortality.

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Cervical cancer classification based on a bilinear convolutional neural network approach and random projection

Cited by 7 publications

References 32 publications

Inferior Alveolar Nerve Canal Segmentation on CBCT Using U-Net with Frequency Attentions

Inferior Alveolar Nerve Canal Segmentation on CBCT Using U-Net with Frequency Attentions

Differential evolution optimization based ensemble framework for accurate cervical cancer diagnosis

Demystifying COVID-19 mortality causes with interpretable data mining

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