Analysis of Kidney Ultrasound Images Using Deep Learning and Machine Learning Techniques: A Review

George, Mino; Anita, H. B.

doi:10.1007/978-981-16-5640-8_15

Cited by 8 publications

(3 citation statements)

References 62 publications

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“…Deep learning helped build predictive models for the early detection of Kidney Tumors [40]. Researchers have used known pattern analysis methods, such as Modified CNN, GoogleNet, InceptionV3, 3D U-Net, V-Net, Resnet50, Resnet50V2, ReLU, and ImHistNet.…”

Section: Deep Learningmentioning

confidence: 99%

Radiology Imaging Scans for Early Diagnosis of Kidney Tumors: A Review of Data Analytics-Based Machine Learning and Deep Learning Approaches

Gharaibeh

Alzu’bi

Abdullah

et al. 2022

BDCC

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Plenty of disease types exist in world communities that can be explained by humans’ lifestyles or the economic, social, genetic, and other factors of the country of residence. Recently, most research has focused on studying common diseases in the population to reduce death risks, take the best procedure for treatment, and enhance the healthcare level of the communities. Kidney Disease is one of the common diseases that have affected our societies. Sectionicularly Kidney Tumors (KT) are the 10th most prevalent tumor for men and women worldwide. Overall, the lifetime likelihood of developing a kidney tumor for males is about 1 in 466 (2.02 percent) and it is around 1 in 80 (1.03 percent) for females. Still, more research is needed on new diagnostic, early, and innovative methods regarding finding an appropriate treatment method for KT. Compared to the tedious and time-consuming traditional diagnosis, automatic detection algorithms of machine learning can save diagnosis time, improve test accuracy, and reduce costs. Previous studies have shown that deep learning can play a role in dealing with complex tasks, diagnosis and segmentation, and classification of Kidney Tumors, one of the most malignant tumors. The goals of this review article on deep learning in radiology imaging are to summarize what has already been accomplished, determine the techniques used by the researchers in previous years in diagnosing Kidney Tumors through medical imaging, and identify some promising future avenues, whether in terms of applications or technological developments, as well as identifying common problems, describing ways to expand the data set, summarizing the knowledge and best practices, and determining remaining challenges and future directions.

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Section: Deep Learningmentioning

confidence: 99%

Radiology Imaging Scans for Early Diagnosis of Kidney Tumors: A Review of Data Analytics-Based Machine Learning and Deep Learning Approaches

Gharaibeh

Alzu’bi

Abdullah

et al. 2022

BDCC

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“…Noise Removal https://mol2net-08.sciforum.net/ affect either kidney. "Nephrolithiasis" comes from the Greek words "Nephros" (which means kidney) and "Lithos," which means "stone" (stone) [1]. Nephrolithiasis may vary from as little as a single grain of sand to as big as a single salt particle.…”

Section: Nephrolithiasis Ultrasound Imagesmentioning

confidence: 99%

Analysis and Identification of Nephrolithiasis from Ultrasound Images using Machine Learning Approach

NAEEM

ALI

Anam

2022

Proceedings of MOL2NET'22, Conference on Molecular, Biomedical &Amp; Computational Sciences and Engineering, 8th Ed. - MOL2NET:

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Nephrolithiasis, commonly known as kidney stone disease, is a disorder in which the deposition of certain minerals causes a stone to develop in the urinary tract. Urolithiasis is another name for nephrolithiasis. Most of the time, kidney calculi form in the renal system and are eliminated through the urine system. Even a tiny stone can readily travel through the urine without any issues. More than 5 millimeters (0.2 inches) in diameter, fully grown calculi can clog the urinary tract, which can cause intense pain in the lumbar region or the stomach.Calculi can result in problems such as dysuria, vomiting, and hematuria. The recommended approach for automatically segmenting kidney stones is based on a four-stage framework, the first of which calls for preprocessing kidney pictures for better enhancement and is followed by the active contour method for automatically segmenting kidney stones. In the future, an assessment will be performed depending on the size and kind of stone.

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“…Much activity has been devoted to automate thyroid detection through CAD systems, mainly based on CNN. • Kidneys [207][208][209][210][211][212][213][214][215][216][217][218][219][220]: US image-based diagnosis are widely used for the detection of kidney abnormalities including cysts and tumors. For the early diagnosis of kidney diseases, DNN and SVM are very often used as machine learning models for abnormality detection and classification.…”

mentioning

confidence: 99%

Recent Advances in Machine Learning Applied to Ultrasound Imaging

Micucci

Iula

2022

Electronics

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Machine learning (ML) methods are pervading an increasing number of fields of application because of their capacity to effectively solve a wide variety of challenging problems. The employment of ML techniques in ultrasound imaging applications started several years ago but the scientific interest in this issue has increased exponentially in the last few years. The present work reviews the most recent (2019 onwards) implementations of machine learning techniques for two of the most popular ultrasound imaging fields, medical diagnostics and non-destructive evaluation. The former, which covers the major part of the review, was analyzed by classifying studies according to the human organ investigated and the methodology (e.g., detection, segmentation, and/or classification) adopted, while for the latter, some solutions to the detection/classification of material defects or particular patterns are reported. Finally, the main merits of machine learning that emerged from the study analysis are summarized and discussed.

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Analysis of Kidney Ultrasound Images Using Deep Learning and Machine Learning Techniques: A Review

Cited by 8 publications

References 62 publications

Radiology Imaging Scans for Early Diagnosis of Kidney Tumors: A Review of Data Analytics-Based Machine Learning and Deep Learning Approaches

Radiology Imaging Scans for Early Diagnosis of Kidney Tumors: A Review of Data Analytics-Based Machine Learning and Deep Learning Approaches

Analysis and Identification of Nephrolithiasis from Ultrasound Images using Machine Learning Approach

Recent Advances in Machine Learning Applied to Ultrasound Imaging

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