Hybrid ResNet-ViT Framework for Endometrial Lesion Analysis: A Comparative Study of MRI and CT in Endometrial Cancer Classification
Bibliographic record
Abstract
Omar H Abu-azzam,1 Amer Mahmoud Sindiani,2 Salem Alhatamleh,3 Mohammad Amin,3 Hamad Yahia Abu Mhanna,4 Rola Madain,2 Hanan Fawaz Akhdar,5 Hasan Gharaibeh,6 Omar F Altal,2 Eman Hussein Alshdaifat,7 Tarfah Majed Alinad,5 Fatimah Maashey,5 Ahmad Nasayreh,6 Ayah Bashkami,8 Latifah Alghulayqah5 1Department of Obstetrics and Gynecology, Faculty of Medicine, Mutah University, Al-Karak, Jordan; 2Department of Obstetrics and Gynecology, Faculty of Medicine, Jordan University of Science and Technology, Irbid, Jordan; 3Computer Science Department, Faculty of Information Technology and Computer Sciences, Yarmouk University, Irbid, Jordan; 4Department of Medical Imaging, Faculty of Allied Medical Sciences, Isra University, Amman, Jordan; 5Physics Department, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia; 6Artificial Intelligence and Data Innovation Office, King Hussein Cancer Center, Amman, Jordan; 7Department of Obstetrics and Gynecology, Faculty of Medicine, Yarmouk University, Irbid, Jordan; 8Department of Medical Laboratory Sciences, Al Balqa Applied University, Salt, JordanCorrespondence: Hanan Fawaz Akhdar, Email hfakdar@imamu.edu.saAim: This study aimed to evaluate and compare the diagnostic performance of computed tomography (CT) and magnetic resonance imaging (MRI) in the detection of endometrial cancer, using a deep learning approach.Methods: Two endometrial image sets were obtained from King Abdullah University Hospital: the KAUH Endometrial Cancer MRI dataset (KAUH-ECMD) and the KAUH Endometrial Cancer CT dataset (KAUH-ECCTD), collected from 300 patients aged between 22 and 85. A hybrid deep learning model combining ResNet50 and Vision Transformer (ViT) was applied to classify the images into three categories: benign, malignant, and normal.Results: The proposed ViTNet model achieved an accuracy of 90.24% in detecting endometrial cancer using MRI images and 86.99% using CT images. The MRI-based approach demonstrated superior diagnostic performance in detecting endometrial cancer compared to CT-based classification.Conclusion: Deep learning models utilizing MRI and CT images demonstrate high accuracy in classifying endometrial cases. MRI in particular shows promise in supporting diagnostic workflows. Future work will focus on further validating the model’s ability to evaluate depth of invasion and other prognostic features.Keywords: endometrial neoplasms, magnetic resonance imaging, computed tomography, deep learning, radiomics
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".