MSAD-Net：用于肺癌分类的多尺度和空间注意力密集网络
MSAD-Net: Multiscale and Spatial Attention-based Dense Network for Lung Cancer Classification
AI总结：
提出MSAD-Net，一种带空间注意力的多尺度密集CNN，通过新颖密集模块和跳跃连接解决类别不平衡，在肺癌分类上显著超越现有模型。
摘要：
肺癌是一种起源于肺组织的严重恶性肿瘤，若未在早期发现，可能致命。它在全球癌症相关死亡原因中名列前茅。使用胸部X射线图像或计算机断层扫描（CT）扫描图像手动检测肺癌给放射科医生带来了巨大挑战。因此，需要从放射学图像中自动诊断肺癌的系统。随着深度学习的近期兴起，特别是通过卷积神经网络（CNN），肺癌的自动检测已变得更加简单。然而，许多研究人员指出，传统CNN的性能可能因类别不平衡问题而受到阻碍，这在医学图像中普遍存在。在本研究工作中，我们提出了一种新颖的CNN架构“多尺度密集网络（MSD-Net）”（从头训练）。我们带来的新颖性包括：（I）我们在CNN模型的第4块和第5块中引入了新颖的密集模块。我们在每个密集模块中利用了3个深度可分离卷积（DWSC）层和一个1x1卷积层，以显著降低模型复杂度。（II）此外，我们加入了一个从第3块到第5块的跳跃连接，以及一个从第4块到全局平均池化（GAP）层的并行分支连接。我们在最后一个并行分支中使用了膨胀卷积层（膨胀率=2）以提取多尺度特征。大量实验表明，我们提出的模型在性能上显著优于最新的CNN模型ConvNext-Tiny、近期趋势的视觉Transformer（ViT）、基于池化的ViT（PiT）以及其他现有模型。
英文摘要：
Lung cancer, a severe form of malignant tumor that originates in the tissues of the lungs, can be fatal if not detected in its early stages. It ranks among the top causes of cancer-related mortality worldwide. Detecting lung cancer manually using chest X-Ray image or Computational Tomography (CT) scans image poses significant challenges for radiologists. Hence, there is a need for automatic diagnosis system of lung cancers from radiology images. With the recent emergence of deep learning, particularly through Convolutional Neural Networks (CNNs), the automated detection of lung cancer has become a much simpler task. Nevertheless, numerous researchers have addressed that the performance of conventional CNNs may be hindered due to class imbalance issue, which is prevalent in medical images. In this research work, we have proposed a novel CNN architecture ``Multi-Scale Dense Network (MSD-Net)'' (trained-from-scratch). The novelties we bring in the proposed model are (I) We introduce novel dense modules in the 4th block and 5th block of the CNN model. We have leveraged 3 depthwise separable convolutional (DWSC) layers, and one 1x1 convolutional layer in each dense module, in order to reduce complexity of the model considerably. (II) Additionally, we have incorporated one skip connection from 3rd block to 5th block and one parallel branch connection from 4th block to Global Average Pooling (GAP) layer. We have utilized dilated convolutional layer (with dilation rate=2) in the last parallel branch in order to extract multi-scale features. Extensive experiments reveal that our proposed model has outperformed latest CNN model ConvNext-Tiny, recent trend Vision Transformer (ViT), Pooling-based ViT (PiT), and other existing models by significant margins.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
