The worldwide COVID-19 pandemic has profoundly influenced the health and
everyday experiences of individuals across the planet. It is a highly
contagious respiratory disease requiring early and accurate detection to curb
its rapid transmission. Initial testing methods primarily revolved around
identifying the genetic composition of the coronavirus, exhibiting a relatively
low detection rate and requiring a time-intensive procedure. To address this
challenge, experts have suggested using radiological imagery, particularly
chest X-rays, as a valuable approach within the diagnostic protocol. This study
investigates the potential of leveraging radiographic imaging (X-rays) with
deep learning algorithms to swiftly and precisely identify COVID-19 patients.
The proposed approach elevates the detection accuracy by fine-tuning with
appropriate layers on various established transfer learning models. The
experimentation was conducted on a COVID-19 X-ray dataset containing 2000
images. The accuracy rates achieved were impressive of 100% for EfficientNetB4
model. The fine-tuned EfficientNetB4 achieved an excellent accuracy score,
showcasing its potential as a robust COVID-19 detection model. Furthermore,
EfficientNetB4 excelled in identifying Lung disease using Chest X-ray dataset
containing 4,350 Images, achieving remarkable performance with an accuracy of
99.17%, precision of 99.13%, recall of 99.16%, and f1-score of 99.14%. These
results highlight the promise of fine-tuned transfer learning for efficient
lung detection through medical imaging, especially with X-ray images. This
research offers radiologists an effective means of aiding rapid and precise
COVID-19 diagnosis and contributes valuable assistance for healthcare
professionals in accurately identifying affected patients.

使用深度学习算法和经过微调的 EfficientNetB4 模型，结合放射学影像技术（尤其是胸部 X 射线）快速准确地识别 COVID-19 患者，并提供了有效的肺部疾病检测方法，为医疗图像诊断领域带来了有益的研究。

通过精细调整的 EfficientNet 深度学习架构优化性能，提升 COVID-19 检测能力

Empowering COVID-19 Detection: Optimizing Performance Through Fine-Tuned  EfficientNet Deep Learning Architecture

In the last few years, several techniques for facial manipulation in videos
have been successfully developed and made available to the masses (i.e.,
FaceSwap, deepfake, etc.). These methods enable anyone to easily edit faces in
video sequences with incredibly realistic results and a very little effort.
Despite the usefulness of these tools in many fields, if used maliciously, they
can have a significantly bad impact on society (e.g., fake news spreading,
cyber bullying through fake revenge porn). The ability of objectively detecting
whether a face has been manipulated in a video sequence is then a task of
utmost importance. In this paper, we tackle the problem of face manipulation
detection in video sequences targeting modern facial manipulation techniques.
In particular, we study the ensembling of different trained Convolutional
Neural Network (CNN) models. In the proposed solution, different models are
obtained starting from a base network (i.e., EfficientNetB4) making use of two
different concepts: (i) attention layers; (ii) siamese training. We show that
combining these networks leads to promising face manipulation detection results
on two publicly available datasets with more than 119000 videos.

本文提出了一种使用 ensembles 方法来判断现代面部操作技术的视频序列的面部操作检测的解决方案，该方案使用了基于卷积神经网络的不同模型，并结合了 EfficientNetB4、注意力层和连体训练等不同概念，针对以上两个概念结合的模型，本文在两个公开数据集上测试得出了令人期待的结果。