Efficient Lung Cancer Image Classification and Segmentation Algorithm Based on an Improved Swin Transformer

Sun, Ruina; Yuexin, Pang,; Li, Wenfa

doi:10.3390/electronics12041024

Cited by 28 publications

(11 citation statements)

References 35 publications

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“…This study demonstrates the use of distributed computation, federated learning, and complicated mathematical representations to protect patient privacy, facilitate group decision-making, and manage large-scale medical records. In [4], Ruina Sun et al outlined the authors' method for classifying and segmenting pictures of lung cancer using an enhanced Swin Transformer model. The study found that the Swin Transformer model outperformed other models on tasks like as lung cancer segmentation and classification.…”

Section: Literature Reviewmentioning

confidence: 99%

Lrfe: A Novel Local Response Feature Elimination Process for Identification of Lung Cancer Cells

Chintha Vishnu Vardhana Reddy

2024

jes

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One of the main causes of cancer-related mortality across the globe is lung cancer. Early-stage lung cancer frequently exhibits no symptoms, which delays diagnosis until the illness has progressed. Before symptoms manifest, screening and early detection techniques can aid in the early diagnosis and treatment of lung cancer. Many olden research papers have implemented image processing and few latest papers have implemented computer vision techniques to detect lung cancer. Particularly when dealing with minor or subtle anomalies, image processing algorithms may not be able to detect lung cancer lesions with sufficient sensitivity and specificity. It is still difficult to increase the detection algorithms' accuracy and dependability, especially when dealing with early-stage lesions or situations where attributes overlap. It takes a lot of processing power, such as high-performance GPUs and enormous memory capacities, to train deep learning models, particularly large-scale convolutional neural networks (CNNs). In this proposed research, the model pre-processes the images using the ostu and sober filter mechanisms because Otsu's approach adjusts to the features of the input image, including noise, contrast, and lighting fluctuations. It is capable of handling images with varying dynamic ranges and intensity distributions without depending on pre-established threshold settings. When it comes to image noise, the Sobel filter is more resilient than other edge detection methods. It produces clearer edge maps and fewer false detections by determining the gradient magnitude, which amplifies edge information while suppressing noise. The features are extracted using the tuned AlexNet pre-trained model, in AlexNet there is a layer known as “Layer-wise Relevance Propagation”. By giving each pixel or feature in the input image a relevance score, the LRP layer offers fine-grained feature attribution. This makes it possible to analyze in great depth which particular elements or areas of the input image are most important for the network to forecast, which helps to clarify the underlying patterns that the network has learned. At last, the extracted features are further reduced using the enhanced feature elimination method. By iteratively selecting subsets of features based on their importance, RFE helps to identify the most relevant features for the classification task. Integrating RFE with SVM classifiers can lead to improved model performance by focusing on the subset of features that are most discriminative and informative for the classification problem.

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Section: Literature Reviewmentioning

confidence: 99%

Lrfe: A Novel Local Response Feature Elimination Process for Identification of Lung Cancer Cells

Chintha Vishnu Vardhana Reddy

2024

jes

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“…Chen Wei et al presented that HRSTNet [ 29 ] replaces the convolutional layer with a transformer module that creates varied resolution feature mapping information. Ruina Sun et al [ 30 ] introduced an effective image classification segmentation technique based on an enhanced Swin transformer, which was designed specifically for lung cancer classification and segmentation. UNeXt model [ 31 ] was proposed by Vishal M. Patel et al as a deep network architecture.…”

Section: Related Workmentioning

confidence: 99%

Artificial Intelligence-Aided Diagnosis Solution by Enhancing the Edge Features of Medical Images

Liu

et al. 2023

Diagnostics

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Bone malignant tumors are metastatic and aggressive. The manual screening of medical images is time-consuming and laborious, and computer technology is now being introduced to aid in diagnosis. Due to a large amount of noise and blurred lesion edges in osteosarcoma MRI images, high-precision segmentation methods require large computational resources and are difficult to use in developing countries with limited conditions. Therefore, this study proposes an artificial intelligence-aided diagnosis scheme by enhancing image edge features. First, a threshold screening filter (TSF) was used to pre-screen the MRI images to filter redundant data. Then, a fast NLM algorithm was introduced for denoising. Finally, a segmentation method with edge enhancement (TBNet) was designed to segment the pre-processed images by fusing Transformer based on the UNet network. TBNet is based on skip-free connected U-Net and includes a channel-edge cross-fusion transformer and a segmentation method with a combined loss function. This solution optimizes diagnostic efficiency and solves the segmentation problem of blurred edges, providing more help and reference for doctors to diagnose osteosarcoma. The results based on more than 4000 osteosarcoma MRI images show that our proposed method has a good segmentation effect and performance, with Dice Similarity Coefficient (DSC) reaching 0.949, and show that other evaluation indexes such as Intersection of Union (IOU) and recall are better than other methods.

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“…Recently, deep convolutional neural networks have achieved great success in image processing [11][12][13] and have also been introduced into the field of medical imaging [14]. There have been many studies applying deep convolutional neural networks to computer-aided detection systems for pulmonary nodules.…”

Section: Lung Nodule Detectionmentioning

confidence: 99%

3DAGNet: 3D Deep Attention and Global Search Network for Pulmonary Nodule Detection

et al. 2023

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In traditional clinical medicine, respiratory physicians or radiologists often identify the location of lung nodules by highlighting targets in consecutive CT slices, which is labor-intensive and easy-to-misdiagnose work. To achieve intelligent detection and diagnosis of CT lung nodules, we designed a 3D convolutional neural network, called 3DAGNet, for pulmonary nodule detection. Inspired by the diagnostic process of lung nodule localization by physicians, the 3DGNet includes a spatial attention and a global search module. A multi-scale cascade module has also been introduced to enhance the model detection using attention enhancement, global information search, and contextual feature fusion. The experimental results showed that the proposed network achieved accurate detection of lung nodule information, and our method achieves a high sensitivity of 88.08% of the average FROC score on the LUNA16 dataset. In addition, ablation experiments also demonstrated the effectiveness of our method.

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Efficient Lung Cancer Image Classification and Segmentation Algorithm Based on an Improved Swin Transformer

Cited by 28 publications

References 35 publications

Lrfe: A Novel Local Response Feature Elimination Process for Identification of Lung Cancer Cells

Lrfe: A Novel Local Response Feature Elimination Process for Identification of Lung Cancer Cells

Artificial Intelligence-Aided Diagnosis Solution by Enhancing the Edge Features of Medical Images

3DAGNet: 3D Deep Attention and Global Search Network for Pulmonary Nodule Detection

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