One of the most serious corneal disorders, keratoconus is difficult to
diagnose in its early stages and can result in blindness. This illness, which
often appears in the second decade of life, affects people of all sexes and
races. Convolutional neural networks (CNNs), one of the deep learning
approaches, have recently come to light as particularly promising tools for the
accurate and timely diagnosis of keratoconus. The purpose of this study was to
evaluate how well different D-CNN models identified keratoconus-related
diseases. To be more precise, we compared five different CNN-based deep
learning architectures (DenseNet201, InceptionV3, MobileNetV2, VGG19,
Xception). In our comprehensive experimental analysis, the DenseNet201-based
model performed very well in keratoconus disease identification in our
extensive experimental research. This model outperformed its D-CNN equivalents,
with an astounding accuracy rate of 89.14% in three crucial classes:
Keratoconus, Normal, and Suspect. The results demonstrate not only the
stability and robustness of the model but also its practical usefulness in
real-world applications for accurate and dependable keratoconus identification.
In addition, D-CNN DenseNet201 performs extraordinarily well in terms of
precision, recall rates, and F1 scores in addition to accuracy. These measures
validate the model's usefulness as an effective diagnostic tool by highlighting
its capacity to reliably detect instances of keratoconus and to reduce false
positives and negatives.

该研究旨在评估不同的 D-CNN 模型在识别角膜地图贴中所表现出的角膜膨胀症相关疾病方面的性能，并发现 DenseNet201 模型在识别角膜地图贴中的疾病方面表现出色，具有稳定性、健壮性和实际应用性。

基于深度学习的角膜圆锥病变检测

Detection of keratoconus Diseases using deep Learning

The COVID19 pandemic, a unique and devastating respiratory disease outbreak,
has affected global populations as the disease spreads rapidly. Recent Deep
Learning breakthroughs may improve COVID19 prediction and forecasting as a tool
of precise and fast detection, however, current methods are still being
examined to achieve higher accuracy and precision. This study analyzed the
collection contained 8055 CT image samples, 5427 of which were COVID cases and
2628 non COVID. The 9544 Xray samples included 4044 COVID patients and 5500 non
COVID cases. The most accurate models are MobileNet V3 (97.872 percent),
DenseNet201 (97.567 percent), and GoogleNet Inception V1 (97.643 percent). High
accuracy indicates that these models can make many accurate predictions, as
well as others, are also high for MobileNetV3 and DenseNet201. An extensive
evaluation using accuracy, precision, and recall allows a comprehensive
comparison to improve predictive models by combining loss optimization with
scalable batch normalization in this study. Our analysis shows that these
tactics improve model performance and resilience for advancing COVID19
prediction and detection and shows how Deep Learning can improve disease
handling. The methods we suggest would strengthen healthcare systems,
policymakers, and researchers to make educated decisions to reduce COVID19 and
other contagious diseases.
CCS CONCEPTS Covid,Deep Learning, Image Processing
KEYWORDS Covid, Deep Learning, DenseNet201, MobileNet, ResNet, DenseNet,
GoogleNet, Image Processing, Disease Detection.

COVID19 预测和检测的深度学习方法在此研究中得到了改进，并且高精度的预测模型可为医疗系统、决策者和研究者做出准确决策以减少 COVID-19 及其它传染病。