Secondary Pulmonary Tuberculosis Recognition by 4-Direction Varying-Distance GLCM and Fuzzy SVM

Zhang, Yudong; Wang, Wei; Zhang, Xin; Wang, Shuihua

doi:10.1007/s11036-021-01901-7

Cited by 9 publications

(5 citation statements)

References 33 publications

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“…Besides, a slight adaption of multiple-way data augmentation is used to boost the training set. The 10 runs of 10-fold cross-validation demonstrate that this F3 model achieves better scores than SOTA methods on both sensitivity, specificity, precision, accuracy, F1 score, MCC, FMI, and AUC [9].…”

Section: Image Based Information Recognitionmentioning

confidence: 91%

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Advanced Machine Learning Based Mobile Multimedia Application

Chen

Liu

2022

Mobile Netw Appl

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Section: Image Based Information Recognitionmentioning

confidence: 91%

“…The first section of this issue includes five papers, which focuses on the novel image recognition methods, such as novel deep learning models, medical image recognition, etc. [6][7][8][9][10].…”

Section: Image Based Information Recognitionmentioning

confidence: 99%

Advanced Machine Learning Based Mobile Multimedia Application

Chen

Liu

2022

Mobile Netw Appl

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“…This combined matrix P ( i , j ) reduces the number of calculated features from 16 direction dependent to four direction invariant ones, therefore, there is no need to apply a feature selection method since the number of features fed to the classifier is limited. It is common in literature to use direction invariante COM and that has the advantage of reducing the number of calculated features and producing features that are insensitive to ROI orientation 24 . The notation of distance d has been dropped from the invariant matrix notation P ( i , j ) since only one distance d = 1 was used.…”

Section: Methodsmentioning

confidence: 99%

“…It is common in literature to use direction invariante COM and that has the advantage of reducing the number of calculated features and producing features that are insensitive to ROI orientation. 24 The notation of distance d has been dropped from the invariant matrix notation P(i, j) since only one distance d = 1 was used.…”

Section: Cooccurrence Matrix Calculation and Feature Extractionmentioning

confidence: 99%

The effect of quantization at different resolutions on the classification of lesion texture in dermoscopic images using support vector machine

Mahmoud‐Ghoneim

2022

Int J Imaging Syst Tech

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Dermoscopic images have been recently used for automated diagnosis of skin lesions with successful results and a potential for widescale applications.Cooccurrence matrix (COM) is a statistical method of texture feature extraction that remains possibly the most interpretable and expandable method in its category. COM is investigated on four groups of dermoscopic images (high resolution ROI: HRR, medium resolution ROI: MRR, low resolution ROI: LRR, and mixed resolution: MixR), using three different gray level quantization values (b = 25, 15, and 8). Three skin regions were outlined: melanoma, common nevi, and normal skin. Texture features were extracted then Support Vector Machine (SVM) was used as a machine learning classification algorithm. Sensitivity, specificity, and the area under the ROC curve (AUC), were calculated and compared. In the classification processes that involved either melanoma or common nevi against normal skin, MRR at quantization value b = 15 showed an effective and a high-performance choice. On the other hand, when classification involved the two lesions, melanoma, and common nevi, quantization value b = 25 showed best results at both HRR and MRR. A high quantization value at intermediate resolution shows to be an effective choice that balances performance with calculation cost. In all cases, quantization at b = 8 deteriorated the classification output even at high resolution. MixR showed good results when normal skin was classified against either type of lesion. In conclusion, selecting the appropriate quantization is a crucial factor that outweighs resolution effect by itself, and can significantly alter the SVM-COM model performance in automated skin cancer diagnosis.

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“…The SVM algorithm is a supervised algorithm that solves many tasks, including classification tasks. SVM distributes the dataset in n dimensions, where n represents the deep features, and each value of the deep features represents a specific coordinate [37]. The algorithm separates the dataset classes by a hyperplane.…”

Section: Support Vector Machinementioning

confidence: 99%

Deep and Hybrid Learning Technique for Early Detection of Tuberculosis Based on X-ray Images Using Feature Fusion

2022

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Tuberculosis (TB) is a fatal disease in developing countries, with the infection spreading through direct contact or the air. Despite its seriousness, the early detection of tuberculosis by means of reliable techniques can save the patients’ lives. A chest X-ray is a recommended screening technique for locating pulmonary abnormalities. However, analyzing the X-ray images to detect abnormalities requires highly experienced radiologists. Therefore, artificial intelligence techniques come into play to help radiologists to perform an accurate diagnosis at the early stages of TB disease. Hence, this study focuses on applying two AI techniques, CNN and ANN. Furthermore, this study proposes two different approaches with two systems each to diagnose tuberculosis from two datasets. The first approach hybridizes two CNN models, which are Res-Net-50 and GoogLeNet techniques. Prior to the classification stage, the approach applies the principal component analysis (PCA) algorithm to reduce the features’ dimensionality, aiming to extract the deep features. Then, the SVM algorithm is used for classifying features with high accuracy. This hybrid approach achieved superior results in diagnosing tuberculosis based on X-ray images from both datasets. In contrast, the second approach applies artificial neural networks (ANN) based on the fused features extracted by ResNet-50 and GoogleNet models and combines them with the features extracted by the gray level co-occurrence matrix (GLCM), discrete wavelet transform (DWT) and local binary pattern (LBP) algorithms. ANN achieved superior results for the two tuberculosis datasets. When using the first dataset, the ANN, with ResNet-50, GLCM, DWT and LBP features, achieved an accuracy of 99.2%, a sensitivity of 99.23%, a specificity of 99.41%, and an AUC of 99.78%. Meanwhile, with the second dataset, ANN, with the features of ResNet-50, GLCM, DWT and LBP, reached an accuracy of 99.8%, a sensitivity of 99.54%, a specificity of 99.68%, and an AUC of 99.82%. Thus, the proposed methods help doctors and radiologists to diagnose tuberculosis early and increase chances of survival.

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Secondary Pulmonary Tuberculosis Recognition by 4-Direction Varying-Distance GLCM and Fuzzy SVM

Cited by 9 publications

References 33 publications

Advanced Machine Learning Based Mobile Multimedia Application

Advanced Machine Learning Based Mobile Multimedia Application

The effect of quantization at different resolutions on the classification of lesion texture in dermoscopic images using support vector machine

Deep and Hybrid Learning Technique for Early Detection of Tuberculosis Based on X-ray Images Using Feature Fusion

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