Computer-Aided Diagnosis and Staging of Pancreatic Cancer Based on CT Images

Li, Min; Nie, Xiaohan; Reheman, Yilidan; Huang, Pan; Zhang, Shuailei; Yuan, Yushuai; Chen, Chen; Yan, Ziwei; Han, Wei

doi:10.1109/access.2020.3012967

Cited by 19 publications

(4 citation statements)

References 60 publications

(59 reference statements)

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“…We extracted 265-dimensional features using seven handcrafted texture extraction algorithms, including the Hu invariant moments, GLGCM, wavelet transform, GLCM, LBP, GLDS and Markov random field, as shown in Table 2 [18]. (1) LBP…”

Section: Handcrafted Texture Extraction Methodsmentioning

confidence: 99%

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Research on the Auxiliary Classification and Diagnosis of Lung Cancer Subtypes Based on Histopathological Images

Chen

et al. 2021

IEEE Access

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Lung cancer (LC) is one of the most serious cancers threatening human health. Histopathological examination is the gold standard for qualitative and clinical staging of lung tumors. However, the process for doctors to examine thousands of histopathological images is very cumbersome, especially for doctors with less experience. Therefore, objective pathological diagnosis results can effectively help doctors choose the most appropriate treatment mode, thereby improving the survival rate of patients. For the current problem of incomplete experimental subjects in the computer-aided diagnosis of lung cancer subtypes, this study included relatively rare lung adenosquamous carcinoma (ASC) samples for the first time, and proposed a computer-aided diagnosis method based on histopathological images of ASC, lung squamous cell carcinoma (LUSC) and small cell lung carcinoma (SCLC). Firstly, the multidimensional features of 121 LC histopathological images were extracted, and then the relevant features (Relief) algorithm was used for feature selection. The support vector machines (SVMs) classifier was used to classify LC subtypes, and the receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to make it more intuitive evaluate the generalization ability of the classifier. Finally, through a horizontal comparison with a variety of mainstream classification models, experiments show that the classification effect achieved by the Relief-SVM model is the best. The LUSC-ASC classification accuracy was 73.91%, the LUSC-SCLC classification accuracy was 83.91% and the ASC-SCLC classification accuracy was 73.67%. Our experimental results verify the potential of the auxiliary diagnosis model constructed by machine learning (ML) in the diagnosis of LC.

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Section: Handcrafted Texture Extraction Methodsmentioning

confidence: 99%

“…Content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2021.3071057, IEEE Access model obtains optimal performance for LC subtype classification [18]. The main contributions of this study are as follows:…”

Section: Research On the Auxiliary Classification And Diagnosis Of Lung Cancer Subtypes Based On Histopathological Imagesmentioning

confidence: 99%

Research on the Auxiliary Classification and Diagnosis of Lung Cancer Subtypes Based on Histopathological Images

Chen

et al. 2021

IEEE Access

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“…A binary classifier system's performance as its discrimination threshold is changed is graphically represented by a receiver operating characteristic (ROC) curve, or simply ROC curve [30]. It is created by plotting, at various thresholds, the percentage of true positives out of positives (TPR = true positive rate) with the percentage of false positives out of negatives (FPR = false positive rate) [31]. TPR stands for sensitivity, and FPR, or true negative rate, stands for one less than specificity.…”

Section: Roc Curvementioning

confidence: 99%

Mammography-based Computer-Aided Diagnostics for the Identification of Breast Cancer Based on Machine Learning

2024

IJIES

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Breast cancer is the most typical cancer overall and the one that affects women the most frequently. In 2022, there were 2.26 million or more brand-new cases of breast cancer in women. To identify the breast cancer in early stage, it's easy to cure. The apparent structure is examined using computer-aided diagnostic (CAD) technologies. The majority of existing diagnostic approaches rely on mammography mass features; however, the suggested strategy relies on MicroCalcification (MC) characteristics. In this research cancer cells are detected utilizing mammography images, pre-processing methods, segmentation, multi-layer perceptron with artificial algae algorithm and receiver operating characteristics (ROC) curve analysis. The national cancer institute (NCI) of provides mammogram scans. Normalization and median filtering were used as part of the pre-processing procedure. Segmentation is used to detect the location of MicroCalcification present cells using the local threshold approach. The MicroCalcification present cells and MicroCalcification missing cells are classified using the multi-layer perceptron (MPL) with artificial algae algorithm technique and also used to divide MicroCalcification-affected cells into the following categories: initial, very small, small, medium, high, and very high. ROC curve analysis was used to assess system performance. According to experimental findings, for the NCI, University of California Irvine (UCI), nathan kline institute (NKI), investigation of serial studies to predict your 1 (ISPY1), ISPY2, ISPY3, and ISPY4 datasets, the classification using the multi-layer perceptron with artificial algae method has the best accuracy of 96 percent when compared to random forest (RF), MPL with genetic algorithm (GA), hierarchical clustering random forest (HCRF), and convolutional neural network (CNN).

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“…The order and size of G are denoted by n and m respectively. For graph theoretic terminology, we refer to Chartrand and Lesniak [1].…”

Section: By a Graph ( ) G V Ementioning

confidence: 99%

Connected and Total Vertex covering in Graphs

Sathikala¹

2021

TURCOMAT

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A Subset S of vertices of a Graph G is called a vertex cover if S includes at least one end point of every edge of the Graph. A Vertex cover S of G is a connected vertex cover if the induced subgraph of S is connected. The minimum cardinality of such a set is called the connected vertex covering number and it is denoted by . A Vertex cover S of G is a total vertex cover if the induced subgraph of S has no isolates. The minimum cardinality of such a set is called the total vertex covering number and it is denoted by .In this paper a few properties of connected vertex cover and total vertex covers are studied and specific values of and of some well-known graphs are evaluated.

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Computer-Aided Diagnosis and Staging of Pancreatic Cancer Based on CT Images

Cited by 19 publications

References 60 publications

Research on the Auxiliary Classification and Diagnosis of Lung Cancer Subtypes Based on Histopathological Images

Research on the Auxiliary Classification and Diagnosis of Lung Cancer Subtypes Based on Histopathological Images

Mammography-based Computer-Aided Diagnostics for the Identification of Breast Cancer Based on Machine Learning

Connected and Total Vertex covering in Graphs

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