MSAD-Net: Multiscale and Spatial Attention-based Dense Network for Lung Cancer Classification

• URL: http://arxiv.org/abs/2504.14626v1
• Date: Sun, 20 Apr 2025 14:07:21 GMT
• Title: MSAD-Net: Multiscale and Spatial Attention-based Dense Network for Lung Cancer Classification
• Authors: Santanu Roy, Shweta Singh, Palak Sahu, Ashvath Suresh, Debashish Das,
• Abstract summary: Lung cancer ranks among the top causes of cancer-related mortality worldwide.<n>We have proposed a novel CNN architecture Multi-Scale Dense Network (MSD-Net)'' (trained-from-scratch)<n>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.
• Score: 1.5728609542259502
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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