Comparative efficiency analysis of cnn and vit models in brain cancer detection based on MRI images

Nishanov, Akhram Khasanovich, Zaripov, Fazil Makhsetovich, Qambarov, Bahriddin, Jurakulov, Nodirbek Sobirovich, Makhamatdinov, Abdul-Aziz Karamatdinovich

Al-Farg'oniy avlodlari · 2026-yil

Annotatsiya

In this study, the task of classifying brain tumors based on magnetic resonance imaging was analyzed using deep learning approaches. The study compared convolutional neural networks and transformer-based models, including the Vision Transformer and DEiT architectures. The experiments were conducted on an open "Brain Tumor MRI Dataset" dataset, and the models were evaluated based on accuracy, precision, recall, and F1-score metrics. According to the results, the DEiT model demonstrated the highest efficiency, achieving 98.04% validation and 94.44% testing accuracy. The results obtained showed the superiority of transformer models due to their ability to effectively study global properties

Maqola ma’lumotlari
MualliflarNishanov, Akhram Khasanovich, Zaripov, Fazil Makhsetovich, Qambarov, Bahriddin, Jurakulov, Nodirbek Sobirovich, Makhamatdinov, Abdul-Aziz Karamatdinovich
JurnalAl-Farg'oniy avlodlari
NashriyotТАТУ Фарғона филиали
ISSN2181-4252
Nashr sanasi2026-06-08
Son2
Betlar357-363
TilIngliz
Ilmiy sohaTexnika
Indeksga qo‘shilgan2026-08-26

Maqolaning asl sahifasi

Kalit so‘zlar

MRI, brain tumor, deep learning, CNN, Vision Transformer, DEiT, classification, MRI, brain tumor, deep learning, CNN, Vision Transformer, DEiT, classification

Ilmiy soha

Texnika

Iqtibos keltirish

APANishanov, Akhram Khasanovich, Zaripov, Fazil Makhsetovich, Qambarov, Bahriddin, Jurakulov, Nodirbek Sobirovich, & Makhamatdinov, Abdul-Aziz Karamatdinovich (2026). COMPARATIVE EFFICIENCY ANALYSIS OF CNN AND VIT MODELS IN BRAIN CANCER DETECTION BASED ON MRI IMAGES. Al-Farg'oniy avlodlari, (2), 357-363. https://al-fargoniy.uz/index.php/journal/article/view/1206
GOST (OAK)Nishanov, Akhram Khasanovich va b. COMPARATIVE EFFICIENCY ANALYSIS OF CNN AND VIT MODELS IN BRAIN CANCER DETECTION BASED ON MRI IMAGES // Al-Farg'oniy avlodlari. – 2026. – № 2. – P. 357-363.
MLANishanov, Akhram Khasanovich, Zaripov, Fazil Makhsetovich, Qambarov, Bahriddin, Jurakulov, Nodirbek Sobirovich, and Makhamatdinov, Abdul-Aziz Karamatdinovich. “COMPARATIVE EFFICIENCY ANALYSIS OF CNN AND VIT MODELS IN BRAIN CANCER DETECTION BASED ON MRI IMAGES.” Al-Farg'oniy avlodlari, (2), 357-363, 2026. https://al-fargoniy.uz/index.php/journal/article/view/1206
ChicagoNishanov, Akhram Khasanovich, Zaripov, Fazil Makhsetovich, Qambarov, Bahriddin, Jurakulov, Nodirbek Sobirovich, and Makhamatdinov, Abdul-Aziz Karamatdinovich. “COMPARATIVE EFFICIENCY ANALYSIS OF CNN AND VIT MODELS IN BRAIN CANCER DETECTION BASED ON MRI IMAGES.” Al-Farg'oniy avlodlari, (2), 357-363 (2026). https://al-fargoniy.uz/index.php/journal/article/view/1206
HarvardNishanov, Akhram Khasanovich, Zaripov, Fazil Makhsetovich, Qambarov, Bahriddin, Jurakulov, Nodirbek Sobirovich, and Makhamatdinov, Abdul-Aziz Karamatdinovich (2026) ‘COMPARATIVE EFFICIENCY ANALYSIS OF CNN AND VIT MODELS IN BRAIN CANCER DETECTION BASED ON MRI IMAGES’, Al-Farg'oniy avlodlari, (2), 357-363. https://al-fargoniy.uz/index.php/journal/article/view/1206
BibTeX
@article{nishanovakhramkhasanovich2026,
  title = {COMPARATIVE EFFICIENCY ANALYSIS OF CNN AND VIT MODELS IN BRAIN CANCER DETECTION BASED ON MRI IMAGES},
  author = {Nishanov, Akhram Khasanovich and Zaripov, Fazil Makhsetovich and Qambarov, Bahriddin and Jurakulov, Nodirbek Sobirovich and Makhamatdinov, Abdul-Aziz Karamatdinovich},
  journal = {Al-Farg'oniy avlodlari},
  year = {2026},
  number = {2},
  pages = {357-363},
  url = {https://al-fargoniy.uz/index.php/journal/article/view/1206}
}

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