Auxiliary Diagnosis of Lung Cancer with Magnetic Resonance Imaging Data under Deep Learning

Xia, Lei

doi:10.1155/2022/1994082

Cited by 3 publications

(3 citation statements)

References 24 publications

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“…Table 11 shows a numeric comparison of how well the new method U-NET++ works with three other deep learning models, U-Net ( 7 ), NU-Net ( 6 ), and WU-Net ( 12 ), using CT images of lung nodules from a dataset that was already made public, the suggested method is better than the average method for segmenting images of lung nodules.…”

Section: Results Discussion and Comparison With Other Modelsmentioning

confidence: 99%

Hybrid healthcare unit recommendation system using computational techniques with lung cancer segmentation

Albalawi,

Neal Joshua,

Joys

et al. 2024

Front. Med.

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IntroductionOur research addresses the critical need for accurate segmentation in medical healthcare applications, particularly in lung nodule detection using Computed Tomography (CT). Our investigation focuses on determining the particle composition of lung nodules, a vital aspect of diagnosis and treatment planning.MethodsOur model was trained and evaluated using several deep learning classifiers on the LUNA-16 dataset, achieving superior performance in terms of the Probabilistic Rand Index (PRI), Variation of Information (VOI), Region of Interest (ROI), Dice Coecient, and Global Consistency Error (GCE).ResultsThe evaluation demonstrated a high accuracy of 91.76% for parameter estimation, confirming the effectiveness of the proposed approach.DiscussionOur investigation focuses on determining the particle composition of lung nodules, a vital aspect of diagnosis and treatment planning. We proposed a novel segmentation model to identify lung disease from CT scans to achieve this. We proposed a learning architecture that combines U-Net with a Two-parameter logistic distribution for accurate image segmentation; this hybrid model is called U-Net++, leveraging Contrast Limited Adaptive Histogram Equalization (CLAHE) on a 5,000 set of CT scan images.

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Section: Results Discussion and Comparison With Other Modelsmentioning

confidence: 99%

Hybrid healthcare unit recommendation system using computational techniques with lung cancer segmentation

Albalawi,

Neal Joshua,

Joys

et al. 2024

Front. Med.

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“…The convolutional neural network segmentation model treats each patient's CT image as an array unit, utilizing GPU acceleration to simultaneously process multiple layers of data. In contrast, manual segmentation requires layer-by-layer segmentation, resulting in lower efficiency [30,31]. Unlike traditional 2D convolutional neural networks, the 3D convolutional neural network employed in this study accounts for spatial and contextual information between CT slices.…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of an Artificial Intelligence Preoperative Planning and Patient-Specific Instrumentation System for Total Knee Arthroplasty

Li,

Liu,

Chen

et al. 2023

Bioengineering

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Background: Accurate preoperative planning for total knee arthroplasty (TKA) is crucial. Computed tomography (CT)-based preoperative planning offers more comprehensive information and can also be used to design patient-specific instrumentation (PSI), but it requires well-reconstructed and segmented images, and the process is complex and time-consuming. This study aimed to develop an artificial intelligence (AI) preoperative planning and PSI system for TKA and to validate its time savings and accuracy in clinical applications. Methods: The 3D-UNet and modified HRNet neural network structures were used to develop the AI preoperative planning and PSI system (AIJOINT). Forty-two patients who were scheduled for TKA underwent both AI and manual CT processing and planning for component sizing, 20 of whom had their PSIs designed and applied intraoperatively. The time consumed and the size and orientation of the postoperative component were recorded. Results: The Dice similarity coefficient (DSC) and loss function indicated excellent performance of the neural network structure in CT image segmentation. AIJOINT was faster than conventional methods for CT segmentation (3.74 ± 0.82 vs. 128.88 ± 17.31 min, p < 0.05) and PSI design (35.10 ± 3.98 vs. 159.52 ± 17.14 min, p < 0.05) without increasing the time for size planning. The accuracy of AIJOINT in planning the size of both femoral and tibial components was 92.9%, while the accuracy of the conventional method in planning the size of the femoral and tibial components was 42.9% and 47.6%, respectively (p < 0.05). In addition, AI-based PSI improved the accuracy of the hip–knee–ankle angle and reduced postoperative blood loss (p < 0.05). Conclusion: AIJOINT significantly reduces the time needed for CT processing and PSI design without increasing the time for size planning, accurately predicts the component size, and improves the accuracy of lower limb alignment in TKA patients, providing a meaningful supplement to the application of AI in orthopaedics.

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“…MRI has a high diagnostic value for hypertrophy tumors [61] and lung cancer staging [62,63]. MRI is significantly better than CT in the diagnosis of the staging of bladder cancer, prostate cancer, and cervical cancer [64,65].…”

Section: Magnetic Resonance Imagingmentioning

confidence: 99%

Deep Learning for Medical Image-Based Cancer Diagnosis

Jiang

Wang

et al. 2023

Cancers

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(1) Background: The application of deep learning technology to realize cancer diagnosis based on medical images is one of the research hotspots in the field of artificial intelligence and computer vision. Due to the rapid development of deep learning methods, cancer diagnosis requires very high accuracy and timeliness as well as the inherent particularity and complexity of medical imaging. A comprehensive review of relevant studies is necessary to help readers better understand the current research status and ideas. (2) Methods: Five radiological images, including X-ray, ultrasound (US), computed tomography (CT), magnetic resonance imaging (MRI), positron emission computed tomography (PET), and histopathological images, are reviewed in this paper. The basic architecture of deep learning and classical pretrained models are comprehensively reviewed. In particular, advanced neural networks emerging in recent years, including transfer learning, ensemble learning (EL), graph neural network, and vision transformer (ViT), are introduced. Five overfitting prevention methods are summarized: batch normalization, dropout, weight initialization, and data augmentation. The application of deep learning technology in medical image-based cancer analysis is sorted out. (3) Results: Deep learning has achieved great success in medical image-based cancer diagnosis, showing good results in image classification, image reconstruction, image detection, image segmentation, image registration, and image synthesis. However, the lack of high-quality labeled datasets limits the role of deep learning and faces challenges in rare cancer diagnosis, multi-modal image fusion, model explainability, and generalization. (4) Conclusions: There is a need for more public standard databases for cancer. The pre-training model based on deep neural networks has the potential to be improved, and special attention should be paid to the research of multimodal data fusion and supervised paradigm. Technologies such as ViT, ensemble learning, and few-shot learning will bring surprises to cancer diagnosis based on medical images.

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Auxiliary Diagnosis of Lung Cancer with Magnetic Resonance Imaging Data under Deep Learning

Cited by 3 publications

References 24 publications

Hybrid healthcare unit recommendation system using computational techniques with lung cancer segmentation

Hybrid healthcare unit recommendation system using computational techniques with lung cancer segmentation

Development and Validation of an Artificial Intelligence Preoperative Planning and Patient-Specific Instrumentation System for Total Knee Arthroplasty

Deep Learning for Medical Image-Based Cancer Diagnosis

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