Lung diseases remain a critical global health concern, and it's crucial to
have accurate and quick ways to diagnose them. This work focuses on classifying
different lung diseases into five groups: viral pneumonia, bacterial pneumonia,
COVID, tuberculosis, and normal lungs. Employing advanced deep learning
techniques, we explore a diverse range of models including CNN, hybrid models,
ensembles, transformers, and Big Transfer. The research encompasses
comprehensive methodologies such as hyperparameter tuning, stratified k-fold
cross-validation, and transfer learning with fine-tuning.Remarkably, our
findings reveal that the Xception model, fine-tuned through 5-fold
cross-validation, achieves the highest accuracy of 96.21\%. This success shows
that our methods work well in accurately identifying different lung diseases.
The exploration of explainable artificial intelligence (XAI) methodologies
further enhances our understanding of the decision-making processes employed by
these models, contributing to increased trust in their clinical applications.

通过深度学习技术，本研究旨在将不同的肺部疾病分为五类，并以 96.21% 的准确率通过 Xception 模型进行分类和诊断。

利用深度学习和可解释人工智能技术对胸部 X 光图像进行可解释性肺疾病分类

Explainable Lung Disease Classification from Chest X-Ray Images  Utilizing Deep Learning and XAI

Intracranial hemorrhages in head CT scans serve as a first line tool to help
specialists diagnose different types. However, their types have diverse shapes
in the same type but similar confusing shape, size and location between types.
To solve this problem, this paper proposes an all attention U-Net. It uses
channel attentions in the U-Net encoder side to enhance class specific feature
extraction, and space and channel attentions in the U-Net decoder side for more
accurate shape extraction and type classification. The simulation results show
up to a 31.8\% improvement compared to baseline, ResNet50 + U-Net, and better
performance than in cases with limited attention.

本文提出了一种全注意力 U-Net 模型，通过使用通道注意力和空间通道注意力在 U-Net 编码器和解码器的不同侧面，实现了对头部 CT 扫描中颅内出血类型的准确形状提取和分类，模拟结果显示相比基线（ResNet50 + U-Net），改进了 31.8％，并在注意力有限的情况下取得了更好的性能。

全关注 U-NET 用于头部 CT 图像中颅内出血的语义分割

All Attention U-NET for Semantic Segmentation of Intracranial  Hemorrhages In Head CT Images

Eye is the essential sense organ for vision function. Due to the fact that
certain eye disorders might result in vision loss, it is essential to diagnose
and treat eye diseases early on. By identifying common eye illnesses and
performing an eye check, eye care providers can safeguard patients against
vision loss or blindness. Convolutional neural networks (CNN) and transfer
learning were employed in this study to discriminate between a normal eye and
one with diabetic retinopathy, cataract, or glaucoma disease. Using transfer
learning for multi-class classification, high accuracy was achieved at 94%
while the traditional CNN achieved 84% rate.

通过识别普通眼病并进行眼部检查，本研究使用卷积神经网络和迁移学习区分正常眼睛和患有糖尿病视网膜病变、白内障或青光眼的眼睛，通过迁移学习的多分类方法，实现了 94% 的高准确率，而传统的卷积神经网络则达到了 84% 的准确率。