MedicalRec: Medical recommender system for image classification without retraining
Researchers introduce MedicalRec, a transformer-based recommender system that recommends optimal models for medical image classification without retraining, reducing energy consumption and trial-and-error waste. The system uses a benchmark dataset MedicalRec-Bench with over 5,000 records from 3,000 articles, achieving a maximum HitRate@100 of 75.5%.
[2606.07553] MedicalRec: Medical recommender system for image classification without retraining
[Submitted on 23 May 2026]
Title:MedicalRec: Medical recommender system for image classification without retraining
View a PDF of the paper titled MedicalRec: Medical recommender system for image classification without retraining, by Roghayeh Taghavi and 7 other authors
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Abstract:The emergence of machine learning and deep learning has revolutionized the efficiency of diagnostic, therapeutic, and administrative systems in healthcare. However, this rapid adoption has come at the cost of requiring significant computing power and energy consumption, as well as e-waste disposal and carbon emissions. One of the challenges of these models is choosing the right model for classification tasks. To this end, researchers attempt to identify the optimal model using their data through trial and error, which involves energy consumption and waste. The goal of this study is to develop a model-based recommender system for medical image classification. For this purpose, a data set was collected from 3,000 articles in the field of medical image classification. This dataset, publicly available under the name MedicalRec-Bench, contains over 5,000 records of models tested in various tasks, including Skin Cancer Classification, Tumour Classification, Wound Classification, Breast Cancer, and MRI classification. The dataset was evaluated in four different modes, depending on the number of features: MedicalRec I (5 features), MedicalRec II (9 features), MedicalRec III (11 features), and MedicalRec IV (18 features). Collecting all values for the features is challenging due to non-reporting by the authors; hence, the dataset contains significant amounts of missing values. The Medical Recommender System (MedicalRec) is a transformer-based model used for item recommendations in this study. This model achieved remarkable results in the evaluation on the dataset and in the evaluation with 12 base models. This model achieved a maximum HitRate@100 of 75.5%. The dataset and implementations are available through the GitHub link: this https URL
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.07553 [cs.LG]
(or arXiv:2606.07553v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.07553
arXiv-issued DOI via DataCite
Submission history
From: Amir Ali Bengari [view email] [v1] Sat, 23 May 2026 13:29:45 UTC (864 KB)
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