Local Preserving Class Separation Framework to Identify Gestational Diabetes Mellitus Mother Using Ultrasound Fetal Cardiac Image

Gudigar, Anjan; Samanth, Jyothi; Raghavendra, U.; Dharmik, Chinmay; Vasudeva, Akhila; Padmakumar, R; Tan, Ru San; Ciaccio, Edward J.; Molinari, Filippo; Acharya, U. Rajendra

doi:10.1109/access.2020.3042594

Cited by 8 publications

(5 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The classification accuracy of the benign class is high compared to others because the malignant and normal class has fewer images (210 and 133, respectively). In addition, augmentation techniques can be applied to increase the model accuracy [35]. This study applies the rotation operation to balance malignant and normal classes images.…”

Section: Resultsmentioning

confidence: 99%

A Novel Convolutional Neural Networks-Fused Shallow Classifier for Breast Cancer Detection

Alduraibi¹

2022

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

This paper proposes a fused methodology based upon convolutional neural networks and a shallow classifier to diagnose and differentiate breast cancer between malignant lesions and benign lesions. First, various pre-trained convolutional neural networks are used to calculate the features of breast ultrasonography (BU) images. Then, the computed features are used to train the different shallow classifiers like the tree, naïve Bayes, support vector machine (SVM), k-nearest neighbors, ensemble, and neural network. After extensive training and testing, the DenseNet-201, MobileNet-v2, and ResNet-101 trained SVM show high accuracy. Furthermore, the best BU features are merged to increase the classification accuracy at the cost of high computational time. Finally, the feature dimension reduction ReliefF algorithm is applied to address the computational complexity issue. An online publicly available dataset of 780 BU images is utilized to validate the proposed approach. The dataset was further divided into 80 and 20 percent ratios for training and testing the models. After extensive testing and comprehensive analysis, it is found that the DenseNet-201 and Mobile-Net-v2 trained SVM has an accuracy of 90.39% and 94.57% for the original and augmented BU images online dataset, respectively. This study concluded that the proposed framework is efficient and can easily be implemented to help and reduce the workload of radiologists/doctors to diagnose breast cancer in female patients.

show abstract

Section: Resultsmentioning

confidence: 99%

A Novel Convolutional Neural Networks-Fused Shallow Classifier for Breast Cancer Detection

Alduraibi¹

2022

Intelligent Automation &Amp; Soft Computing

View full text Add to dashboard Cite

show abstract

“…Then, to retrieve texture features from the US image data, the gray-level cross matrix (GLCM) was utilized [122,123,126]. The produced features have been further condensed using Fisher's discriminant rate of return [123] as well as native class separation [127].…”

Section: Classification Of Defectsmentioning

confidence: 99%

“…Additionally, a 'bag of words' (BoW) codebook was developed, along with the scale-invariant feature reshape as well as the frequency distribution of optical flow descriptors [128]. Finally, classification models like BPNN [122], this same adaptive neurofuzzy inference system classification model (ANFIS) [123], SVM [127,128], as well as the Gaussian procedure [124] were applied to distinguish between healthy and diseased fetal hearts. The DGACNN was created by combining the DANomaly as well as the GACNN (WGAN-GP as well as CNN) architectures [125].…”

Section: Classification Of Defectsmentioning

confidence: 99%

Investigation on ultrasound images for detection of fetal congenital heart defects

Rufus

2024

Biomed. Phys. Eng. Express

View full text Add to dashboard Cite

Congenital heart defects (CHD) are one of the serious problems that arise during pregnancy. Early CHD detection reduces death rates and morbidity but is hampered by the relatively low detection rates (i.e., 60%) of current screening technology. The detection rate could be increased by supplementing ultrasound imaging with fetal ultrasound image evaluation (FUSI) using deep learning techniques. As a result, the non-invasive foetal ultrasound image has clear potential in the diagnosis of CHD and should be considered in addition to foetal echocardiography. This review paper highlights cutting-edge technologies for detecting CHD using ultrasound images, which involve pre-processing, localization, segmentation, and classification. Existing technique of preprocessing includes spatial domain filter, non-linear mean filter, transform domain filter, and denoising methods based on Convolutional Neural Network (CNN); segmentation includes thresholding-based techniques, region growing-based techniques, edge detection techniques, Artificial Neural Network (ANN) based segmentation methods, non-deep learning approaches and deep learning approaches. The paper also suggests future research directions for improving current methodologies.

show abstract

“…Subsequently, the median filter of size 5 × 5 is applied to improve the quality of ROI and eliminate the noise. Finally, the obtained images are resized to 256 × 256 to preserve the generality and it offers superior results when using handcrafted feature techniques [44,45].…”

Section: Preprocessingmentioning

confidence: 99%

Directional-Guided Motion Sensitive Descriptor for Automated Detection of Hypertension Using Ultrasound Images

Gudigar,

Kadri,

Raghavendra

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

Local Preserving Class Separation Framework to Identify Gestational Diabetes Mellitus Mother Using Ultrasound Fetal Cardiac Image

Cited by 8 publications

References 48 publications

A Novel Convolutional Neural Networks-Fused Shallow Classifier for Breast Cancer Detection

A Novel Convolutional Neural Networks-Fused Shallow Classifier for Breast Cancer Detection

Investigation on ultrasound images for detection of fetal congenital heart defects

Directional-Guided Motion Sensitive Descriptor for Automated Detection of Hypertension Using Ultrasound Images

Contact Info

Product

Resources

About