Ultrasound image assisted diagnosis of hydronephrosis based on CNN neural network

Su, J.; Liu, Yuanhui; Wang, Junmei

doi:10.1016/j.jksus.2020.04.005

Cited by 10 publications

(2 citation statements)

References 33 publications

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“…Recent implementations of machine learning and deep learning in medical ultrasound images have included diverse activities such as classifcation [14], segmentation [15], and detection [8,16]. Ultrasound images of various regions of the body have been used in CAD to diagnose diferent types of illnesses that can threaten human life, such as breast cancer [17], hydronephrosis [18], and prostate cancer [19]. Moreover, many contributions have been carried out by other researchers in order to identify PCOS by using ultrasound images [4,13,14,16,[20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Fusion Approach to Diagnosis the Polycystic Ovary Syndrome (PCOS)

Al-Amoudi

Khan

Aslam

et al. 2023

Applied Computational Intelligence and Soft Computing

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One of the leading causes of female infertility is PCOS, which is a hormonal disorder affecting women of childbearing age. The common symptoms of PCOS include increased acne, irregular period, increase in body hair, and overweight. Early diagnosis of PCOS is essential to manage the symptoms and reduce the associated health risks. Nonetheless, the diagnosis is based on Rotterdam criteria, including a high level of androgen hormones, ovulation failure, and polycystic ovaries on the ultrasound image (PCOM). At present, doctors and radiologists manually perform PCOM detection using ovary ultrasound by counting the number of follicles and determining their volume in the ovaries, which is one of the challenging PCOS diagnostic criteria. Moreover, such physicians require more tests and checks for biochemical/clinical signs in addition to the patient’s symptoms in order to decide the PCOS diagnosis. Furthermore, clinicians do not utilize a single diagnostic test or specific method to examine patients. This paper introduces the data set that includes the ultrasound image of the ovary with clinical data related to the patient that has been classified as PCOS and non-PCOS. Next, we proposed a deep learning model that can diagnose the PCOM based on the ultrasound image, which achieved 84.81% accuracy using the Inception model. Then, we proposed a fusion model that includes the ultrasound image with clinical data to diagnose the patient if they have PCOS or not. The best model that has been developed achieved 82.46% accuracy by extracting the image features using MobileNet architecture and combine with clinical features.

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Section: Introductionmentioning

confidence: 99%

A Deep Learning Fusion Approach to Diagnosis the Polycystic Ovary Syndrome (PCOS)

Al-Amoudi

Khan

Aslam

et al. 2023

Applied Computational Intelligence and Soft Computing

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show abstract

“…Numerous studies on the satisfactory implementation of deep learning in medical image segmentation have been conducted in recent years. CNNs, 8,9,[16][17][18][19][20] U-Net, 10,15,30 U-Net+ +, 12,26 ResU-Net, 11,13,[21][22][23][24] attention U-Net, 14,25,29 and attention ResU-Net [26][27][28]…”

Section: Introductionmentioning

confidence: 99%

Deep‐learning based segmentation of ultrasound adipose image for liposuction

Cai

Liu

Sun

et al. 2023

Robotics Computer Surgery

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BackgroundTo develop an automatic and reliable ultrasonic visual system for robot‐ or computer‐assisted liposuction, we examined the use of deep learning for the segmentation of adipose ultrasound images in clinical and educational settings.MethodsTo segment adipose layers, it is proposed to use an Attention Skip‐Convolutions ResU‐Net (Attention SCResU‐Net) consisting of SC residual blocks, attention gates and U‐Net architecture. Transfer learning is utilised to compensate for the deficiency of clinical data. The Bama pig and clinical human adipose ultrasound image datasets are utilized, respectively.ResultsThe final model obtains a Dice of 99.06 ± 0.95% and an ASD of 0.19 ± 0.18 mm on clinical datasets, outperforming other methods. By fine‐tuning the eight deepest layers, accurate and stable segmentation results are obtained.ConclusionsThe new deep‐learning method achieves the accurate and automatic segmentation of adipose ultrasound images in real‐time, thereby enhancing the safety of liposuction and enabling novice surgeons to better control the cannula.

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Pathological brain classification using multiple kernel-based deep convolutional neural network

Dora,

Agrawal,

Panda

et al. 2023

Neural Comput & Applic

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Ultrasound image assisted diagnosis of hydronephrosis based on CNN neural network

Cited by 10 publications

References 33 publications

A Deep Learning Fusion Approach to Diagnosis the Polycystic Ovary Syndrome (PCOS)

A Deep Learning Fusion Approach to Diagnosis the Polycystic Ovary Syndrome (PCOS)

Deep‐learning based segmentation of ultrasound adipose image for liposuction

Pathological brain classification using multiple kernel-based deep convolutional neural network

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