Classification of Diseases Based on Kidney Computed Tomography Images

Маматов, Н.С., Джалелова, М.М., Джураев, И.А., Файзиев, В.О., Самиджонов, А.Н.

Рақамли технологияларнинг назарий ва амалий масалалари · 2025-yil

Annotatsiya

This research work is devoted to the problem of classifying diseases in kidney computed tomography images using neural networks. The novelty and uniqueness of the research is that it studies not only the traditional binary classification such as “normal/pathological”, but also complex differential diagnosis issues between diseases such as “cyst/stone”, “cyst/tumor”, “stone/tumor”. In the work, binary, ternary and quaternary classification models were used to identify kidney conditions such as normal, cyst, stone, tumor. Depending on the level of complexity of the task, i.e., for binary and ternary classification, architectures such as MobileNetV2 were used, and for quaternary classification, architectures such as VGG16 were used. According to the results, all models achieved high accuracy, with F1-scores of 99.39-100% for the stone class, 98.99-100% for the tumor class, and 99.60% for the cyst/tumor pair. The proposed combinatorial approach, i.e., initially making a general diagnosis using the four-class classification model, and in doubtful cases, conducting additional examination with binary or ternary models, is a reasonable approach for clinical practice. The results of the study will serve as a valuable tool for radiologists and urologists to accurately diagnose kidney diseases and choose the optimal treatment strategy, in accordance with their actual clinical needs.

Maqola ma’lumotlari
MualliflarМаматов, Н.С., Джалелова, М.М., Джураев, И.А., Файзиев, В.О., Самиджонов, А.Н.
JurnalРақамли технологияларнинг назарий ва амалий масалалари
NashriyotТАТУ Самарқанд филиали
ISSN2181-3086, 2181-3094
Nashr sanasi2025-07-25
Jild8
Son2
Betlar37-46
TilIngliz
Ilmiy sohaTexnika
DOI10.62132/ijdt.v8i2.261
Indeksga qo‘shilgan2026-08-26

DOI: 10.62132/ijdt.v8i2.261 · Maqolaning asl sahifasi

Kalit so‘zlar

заболевания почек, сверточная нейронная сеть, компьютерно-томографическое изображение, точность, полнота, f1-оценка, бинарная классификация, многоклассовая классификация, kidney diseases, convolutional neural network, computed tomography image, precision, recall, f1-score, binary classification, multiclass classification

Ilmiy soha

Texnika

Iqtibos keltirish

APAМаматов, Н.С., Джалелова, М.М., Джураев, И.А., Файзиев, В.О., & Самиджонов, А.Н. (2025). Classification of Diseases Based on Kidney Computed Tomography Images. Рақамли технологияларнинг назарий ва амалий масалалари, 8(2), 37-46. https://doi.org/10.62132/ijdt.v8i2.261
GOST (OAK)Маматов, Н.С. va b. Classification of Diseases Based on Kidney Computed Tomography Images // Рақамли технологияларнинг назарий ва амалий масалалари. – 2025. – T. 8, № 2. – P. 37-46. – DOI: 10.62132/ijdt.v8i2.261.
MLAМаматов, Н.С., Джалелова, М.М., Джураев, И.А., Файзиев, В.О., and Самиджонов, А.Н. “Classification of Diseases Based on Kidney Computed Tomography Images.” Рақамли технологияларнинг назарий ва амалий масалалари, 8(2), 37-46, 2025. https://doi.org/10.62132/ijdt.v8i2.261
ChicagoМаматов, Н.С., Джалелова, М.М., Джураев, И.А., Файзиев, В.О., and Самиджонов, А.Н. “Classification of Diseases Based on Kidney Computed Tomography Images.” Рақамли технологияларнинг назарий ва амалий масалалари, 8(2), 37-46 (2025). https://doi.org/10.62132/ijdt.v8i2.261
HarvardМаматов, Н.С., Джалелова, М.М., Джураев, И.А., Файзиев, В.О., and Самиджонов, А.Н (2025) ‘Classification of Diseases Based on Kidney Computed Tomography Images’, Рақамли технологияларнинг назарий ва амалий масалалари, 8(2), 37-46. https://doi.org/10.62132/ijdt.v8i2.261
BibTeX
@article{article2025,
  title = {Classification of Diseases Based on Kidney Computed Tomography Images},
  author = {Маматов, Н.С. and Джалелова, М.М. and Джураев, И.А. and Файзиев, В.О. and Самиджонов, А.Н.},
  journal = {Рақамли технологияларнинг назарий ва амалий масалалари},
  year = {2025},
  volume = {8},
  number = {2},
  pages = {37-46},
  doi = {10.62132/ijdt.v8i2.261},
  url = {https://doi.org/10.62132/ijdt.v8i2.261}
}

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