Diagnosing Kidney Imaging with Deep Learning

Искандарова, С.Н., Махкамова, Д.А.

Рақамли технологияларнинг назарий ва амалий масалалари · 2023-yil

Annotatsiya

Ultrasound images can be used to diagnose kidney disease: identify systemic abnormalities such as cysts, stones, and infections, and provide information about kidney function. This article focuses on the selection of appropriate features for efficient classification of normal and abnormal kidney images. In diagnosing cardiac images, grayscale transformation has been used to classify abnormal images in the kidneys. A data set formed by a convolutional neural network was trained. 2 classes were created and on their basis a recognition result of 89% was achieved. The prevalence of chronic kidney disease (CKD) increases annually in the present scenario of research. One of the sources for further therapy is the CKD prediction where the Machine learning techniques become more important in medical diagnosis due to their high accuracy classification ability. In the recent past, the accuracy of classification algorithms depends on the proper use of algorithms for feature selection to reduce the data size. In this paper, Heterogeneous Modified Artifical Neural Network (HMANN) has been proposed for the early detection, segmentation, and diagnosis of chronic renal failure on the Internet of Medical Things (IoMT) platform. Furthermore, the proposed HMANN is classified as a Support Vector Machine and Multilayer Perceptron (MLP) with a Backpropagation (BP) algorithm. The proposed algorithm works based on an ultrasound image which is denoted as a preprocessing step and the region of kidney interest is segmented in the ultrasound image

Maqola ma’lumotlari
MualliflarИскандарова, С.Н., Махкамова, Д.А.
JurnalРақамли технологияларнинг назарий ва амалий масалалари
NashriyotТАТУ Самарқанд филиали
ISSN2181-3086, 2181-3094
Nashr sanasi2023-03-19
Jild3
Son1
Betlar70-76
TilIngliz
Ilmiy sohaTexnika
Indeksga qo‘shilgan2026-08-26

Maqolaning asl sahifasi

Kalit so‘zlar

image contrast, histogram, image classification, convolutional neural network, CNN (convolutional neural network), neural network, контрастность изображения, гистограмма, классификация изображений, сверхточная нейронная сеть, СНС (сверхточная нейронная сеть), нейронная сеть

Ilmiy soha

Texnika

Iqtibos keltirish

APAИскандарова, С.Н., & Махкамова, Д.А. (2023). Diagnosing Kidney Imaging with Deep Learning. Рақамли технологияларнинг назарий ва амалий масалалари, 3(1), 70-76. https://ijdt.uz/index.php/ijdt/article/view/64
GOST (OAK)Искандарова, С.Н., Махкамова, Д.А. Diagnosing Kidney Imaging with Deep Learning // Рақамли технологияларнинг назарий ва амалий масалалари. – 2023. – T. 3, № 1. – P. 70-76.
MLAИскандарова, С.Н., and Махкамова, Д.А. “Diagnosing Kidney Imaging with Deep Learning.” Рақамли технологияларнинг назарий ва амалий масалалари, 3(1), 70-76, 2023. https://ijdt.uz/index.php/ijdt/article/view/64
ChicagoИскандарова, С.Н., and Махкамова, Д.А. “Diagnosing Kidney Imaging with Deep Learning.” Рақамли технологияларнинг назарий ва амалий масалалари, 3(1), 70-76 (2023). https://ijdt.uz/index.php/ijdt/article/view/64
HarvardИскандарова, С.Н., and Махкамова, Д.А (2023) ‘Diagnosing Kidney Imaging with Deep Learning’, Рақамли технологияларнинг назарий ва амалий масалалари, 3(1), 70-76. https://ijdt.uz/index.php/ijdt/article/view/64
BibTeX
@article{article2023,
  title = {Diagnosing Kidney Imaging with Deep Learning},
  author = {Искандарова, С.Н. and Махкамова, Д.А.},
  journal = {Рақамли технологияларнинг назарий ва амалий масалалари},
  year = {2023},
  volume = {3},
  number = {1},
  pages = {70-76},
  url = {https://ijdt.uz/index.php/ijdt/article/view/64}
}

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