Possible Bias in Supervised Deep Learning Algorithms for CT Lung Nodule Detection and Classification

Sourlos, Nikos; Wang, Jingxuan; Nagaraj, Yeshaswini; Ooijen, Peter M. A. van; Vliegenthart, Rozemarijn

doi:10.3390/cancers14163867

Cited by 22 publications

(11 citation statements)

References 56 publications

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“…With the continuous development of image processing technology, the auxiliary diagnosis software of pulmonary nodules has been applied to a wider extent in the clinic, thereby becoming an important tool in the clinical diagnosis of radiology department. However, the parameters, performance and universality of the software are still different, and the construction of intelligent analysis models for different clinical application purposes has become a research hotspot in this field [ 13 , 14 ] A major difference in the clinical diagnosis methods and the intelligent analysis model is that the latter only extracts feature information from images, and fails to refer to patient complaints, clinical symptoms, medical history and other information, which is insufficient in analyzing information such as vacuoles and calcification inside nodules. This remains an important research direction to improve the clinical diagnosis value of the machine learning algorithm.…”

Section: Discussionmentioning

confidence: 99%

Construction of U-Net++ pulmonary nodule intelligent analysis model based on feature weighted aggregation

Yang

Liu

et al. 2023

THC

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BACKGROUND: Lung cancer is a malignant tumor originating from the bronchial mucosa or glands of the lung. Early lung cancer patients often have no obvious symptoms, but early detection and treatment have an important clinical significance for prognostic effect. Computed tomography (CT) is one of the important means in the diagnosis of lung cancer. In order to better solve the problem of diagnosis efficiency, and reduce the rate of misdiagnosis and missed diagnosis, computer aided diagnosis are employed in the accurate localization and segmentation of pulmonary nodules through imaging diagnostics, image processing technology, and other clinical means. OBJECTIVE: This present study was envisaged to establish an intelligent segmentation model of pulmonary nodules to improve the accuracy of early screening for lung cancer patients. METHODS: Compared with the traditional segmentation model of fully convolutional neural network, the U-Net++ algorithm based on feature-weighted integration (WI-U-Net++) effectively utilized the feature weight information, adopted the adaptive weighted method for weighted integration, and performed an intelligent segmentation of the anatomical structure and image details, which was applied in the auxiliary diagnosis of pulmonary nodules in CT images. Standard chest X-ray phantom was selected as CT scanning objects, and 30 spherical and irregular simulated nodules were built into them, respectively. CT images were collected by setting different tube voltage and noise index, and randomly included into the training set, validation set and test set at a ratio of 8:1:1. RESULTS: The experimental results showed that the segmentation accuracy of WI-U-Net++ algorithm for spheroid nodules and irregular nodules was 98.75% and 83.47%, respectively, which was better than that of U-Net and U-Net++ algorithm. In the auxiliary diagnosis, the recall rate of the WI-U-Net++ algorithm for spheroid nodules and irregular nodules was 93.47% and 84.52%, respectively. The accuracy of the benign or malignant identification was 80.27%, and the AUC was 0.9342. CONCLUSION: U-Net++ algorithm based on feature-weighted integration could improve the segmentation effect of pulmonary nodules. Especially in the case of irregular nodules with malignant signs, the accuracy of clinical diagnosis was significantly improved, and the level of differential diagnosis between benign and malignant was improved.

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Section: Discussionmentioning

confidence: 99%

Construction of U-Net++ pulmonary nodule intelligent analysis model based on feature weighted aggregation

Yang

Liu

et al. 2023

THC

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“…Most of the research in this area is classification (about 36%), followed by segmentation (27%), detection (22%), and others (15%) [30]. Broadly speaking we can list the works in this area in the identification of organs (kidney [48], liver [49,50], lungs [51,52], and heart [53,54]) and in the identification of substructures or lesions (artery calcification [55], nodules [56], polyps [57,58], and lymph nodes [59][60][61]). Among the most commonly used measures to report the performance of the different models are accuracy, sensitivity, specificity, AUC-ROC, and F1 score [1].…”

Section: Computed Tomographymentioning

confidence: 99%

The New Landscape of Diagnostic Imaging with the Incorporation of Computer Vision

Cossio¹

2023

Artificial Intelligence

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Diagnostic medical imaging is a key tool in medical care. In recent years, thanks to advances in computer vision research, a subfield of artificial intelligence, it has become possible to use medical imaging to train and test machine learning models. Among the algorithms investigated, there has been a boom in the use of neural networks since they allow a higher level of automation in the learning process. The areas of medical imaging that have developed the most applications are X-rays, computed tomography, positron emission tomography, magnetic resonance imaging, ultrasonography and pathology. In fact, the COVID-19 pandemic has reshaped the research landscape, especially for radiological and resonance imaging. Notwithstanding the great progress that has been observed in the field, obstacles have also arisen that had to be overcome to continue to improve applications. These obstacles include data protection and the expansion of available datasets, which involves a large investment of resources, time and academically trained manpower.

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“…Additionally, it improves performance and serves as a crucial stage in the data mining process. The outcome of the final data processing may be hampered by data pre-processing [26]. Structure from motion is a method for identifying a scene's 3-D structure from a set of 2-D pictures (SfM) [27].…”

Section: Preprocessingmentioning

confidence: 99%

Detection & Quantification of Lung Nodules Using 3D CT images

Memon

Jawaid

Talpur

2023

IJIST

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In computer vision image detection and quantification play an important role. Image Detection and quantification is the process of identifying nodule position and the amount of covered area. The dataset which we have used for this research contains 3D CT lung images. In our proposed work we have taken 3D images and those are high-resolution images. We have compared the accuracy of the existing mask and our segmented images. The segmentation method that we have applied to these images is Sparse Field Method localized region-based segmentation and for Nodule detection, I have used ray projection. The ray projection method is efficient for making the point more visible by its x, y, and z components. like a parametric equation where the line crossing through a targeted point by that nodule is more dominated. The Frangi filter was to give a geometric shape to the nodule and we got 90% accurate detection. The high mortality rate associated with lung cancer makes it imperative that it be detected at an early stage. The application of computerized image processing methods has the potential to improve both the efficiency and reliability of lung cancer screening. Computerized tomography (CT) pictures are frequently used in medical image processing because of their excellent resolution and low noise. Computer-aided detection systems, including preprocessing and segmentation methods, as well as data analysis approaches, have been investigated in this research for their potential use in the detection and diagnosis of lung cancer. The primary objective was to research cutting-edge methods for creating computational diagnostic tools to aid in the collection, processing, and interpretation of medical imaging data. Nonetheless, there are still areas that need more work, such as improving sensitivity, decreasing false positives, and optimizing the identification of each type of nodule, even those of varying size and form.

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Possible Bias in Supervised Deep Learning Algorithms for CT Lung Nodule Detection and Classification

Cited by 22 publications

References 56 publications

Construction of U-Net++ pulmonary nodule intelligent analysis model based on feature weighted aggregation

Construction of U-Net++ pulmonary nodule intelligent analysis model based on feature weighted aggregation

The New Landscape of Diagnostic Imaging with the Incorporation of Computer Vision

Detection & Quantification of Lung Nodules Using 3D CT images

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