An intelligent validation system for diagnostic and prognosis of ultrasound fetal growth analysis using Neuro-Fuzzy based on genetic algorithm

Kaur, Prabhpreet; Singh, Gurvinder; Kaur, Parmeet

doi:10.1016/j.eij.2018.10.002

Cited by 15 publications

(13 citation statements)

References 36 publications

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“…e spine is the central axis of the trunk and is composed of many vertebrae that are not rigidly joined. It contains four physiological curves: cervical, thoracic, lumbar, and caudal [1]. e canine spine is divided into five vertebral regions: seven cervical (C7), thirteen thoracic (T13), seven lumbar (L7), three sacral (sacrum) (S3), and caudal (Cd variables).…”

Section: Introductionmentioning

confidence: 99%

The Clinical Value of CT Scans for the Early Diagnosis and Treatment of Spinal Fractures and Paraplegia

Jia

Zhu

Guo

2021

Journal of Healthcare Engineering

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The early diagnosis and treatment of spinal fractures and paraplegia by CT scan is investigated in depth and its clinical value is discussed in this paper. In this paper, a novel circulatory generation adversarial network, Spine-GAN, is proposed for the diagnosis of various spinal diseases. The algorithmic model can fully automate the segmentation and classification of multiple spinal structures, such as intervertebral discs, vertebrae, and neuroforamina, simultaneously to intelligently generate a complete clinical diagnosis. The innovation of this method is that Spine-GAN not only overcomes the high variability and complexity of spinal structures in MRI images but also preserves the subtle differences between normal and abnormal spinal structures and dynamically learns obscure but important spatial pathological relationships between adjacent structures of the spine, thus overcoming the limitations of small datasets. Spine-GAN enables accurate segmentation, radiological classification, and pathological correlation representation of the three spinal diseases. Specifically, Spine-GAN achieves a pixel accuracy of 96.2% with a specificity and sensitivity distribution of 89.1% and 86%, respectively. The DMML-Net and Spine-GAN algorithm models have important applications and research values in the clinical diagnosis of spinal diseases and MRI image processing, as well as in the intelligent generation of medical image diagnostic reports, which are of great importance for the study of fine-grained image classification of pathological images. It also has a positive impact on the development of the software.

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Section: Introductionmentioning

confidence: 99%

The Clinical Value of CT Scans for the Early Diagnosis and Treatment of Spinal Fractures and Paraplegia

Jia

Zhu

Guo

2021

Journal of Healthcare Engineering

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“…The table clearly shows the enhancement of our proposed method with respect to PSNR, SNR, S/MSE, EPI and CoC. PSNR of our proposed method is very high which is 5.782%, 8.935%, 11.832%, and 5.141% higher than the existing method 22 with Median, Wiener, Kuan, and Lee filters, respectively. SNR of our proposed method is very high which is 10.284%, 11.31%, 17.258%, and 12.887% higher than the existing method 22 with Median, Wiener, Kuan, and Leefilters, respectively.…”

Section: Resultsmentioning

confidence: 76%

“…Other techniques include contrast enhancement, edge detection, and segmentation, which can help to improve the accuracy of fetal measurements and diagnoses. The researchers mentioned are studying four different de‐noising filters, 22 namely the Wiener filter, median filter, Lee filter, and Kuan filter. These filters are being studied with the aim of improving the quality of fetal ultrasound images by reducing the speckle noise present in them.…”

Section: Proposed Methodologymentioning

confidence: 99%

Fetal growth analysis from ultrasound videos based on different biometrics using optimal segmentation and hybrid classifier

Devisri,

Kavitha

2023

Statistics in Medicine

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Birth defects and their associated deaths, high health and financial costs of maternal care and associated morbidity are major contributors to infant mortality. If permitted by law, prenatal diagnosis allows for intrauterine care, more complicated hospital deliveries, and termination of pregnancy. During pregnancy, a set of measurements is commonly used to monitor the fetal health, including fetal head circumference, crown‐rump length, abdominal circumference, and femur length. Because of the intricate interactions between the biological tissues and the US waves mother and fetus, analyzing fetal US images from a specialized perspective is difficult. Artifacts include acoustic shadows, speckle noise, motion blur, and missing borders. The fetus moves quickly, body structures close, and the weeks of pregnancy vary greatly. In this work, we propose a fetal growth analysis through US image of head circumference biometry using optimal segmentation and hybrid classifier. First, we introduce a hybrid whale with oppositional fruit fly optimization (WOFF) algorithm for optimal segmentation of segment fetal head which improves the detection accuracy. Next, an improved U‐Net design is utilized for the hidden feature (head circumference biometry) extraction which extracts features from the segmented extraction. Then, we design a modified Boosting arithmetic optimization (MBAO) algorithm for feature optimization to selects optimal best features among multiple features for the reduction of data dimensionality issues. Furthermore, a hybrid deep learning technique called bi‐directional LSTM with convolutional neural network (B‐LSTM‐CNN) for fetal growth analysis to compute the fetus growth and health. Finally, we validate our proposed method through the open benchmark datasets are HC18 (Ultrasound image) and oxford university research archive (ORA‐data) (Ultrasound video frames). We compared the simulation results of our proposed algorithm with the existing state‐of‐art techniques in terms of various metrics.

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“…Among them, the degree of optimization of the transportation path directly affects the transportation cost and various indicators in SA. us, the vehicle path problem comes into being [12].…”

Section: Introductionmentioning

confidence: 99%

E‐Commerce Logistics Path Optimization Based on a Hybrid Genetic Algorithm

Yang

2021

Complexity

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Based on the problem of e-commerce logistics and distribution network optimization, this paper summarizes the solution ideas and solutions proposed by domestic and foreign scholars and designs a method to optimize the B2C e-commerce logistics and distribution network by taking into account the special traffic conditions in the city. The logistics network optimization model is established and solved by combining various methods. Taking into account the new target requirements constantly proposed in the modern logistics environment, the vehicle path problem under the generalized objective function is studied, and the multidimensional impact maximization problem in this type of problem is proposed and modeled. The problem follows from the path planning for emergency material delivery. Given locations, roads, and multiple classes of supplies in a map, each road allows vehicles to deliver each class of supplies with a certain probability. The goal of the problem is how to select a finite number of locations in the map as centers of supplies so that the number of locations that can be effectively covered by vehicle paths from them is maximized with the desired probability. For the first time, we used a hybrid genetic algorithm to optimize the e-commerce logistics path, and the optimized results are more reasonable than other algorithms.

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An intelligent validation system for diagnostic and prognosis of ultrasound fetal growth analysis using Neuro-Fuzzy based on genetic algorithm

Cited by 15 publications

References 36 publications

The Clinical Value of CT Scans for the Early Diagnosis and Treatment of Spinal Fractures and Paraplegia

The Clinical Value of CT Scans for the Early Diagnosis and Treatment of Spinal Fractures and Paraplegia

Fetal growth analysis from ultrasound videos based on different biometrics using optimal segmentation and hybrid classifier

E‐Commerce Logistics Path Optimization Based on a Hybrid Genetic Algorithm

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