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arXiv · 2503.21840

Vision Language Models versus Machine Learning Models Performance on Polyp Detection and Classification in Colonoscopy Images

Mohammad Amin Khalafi·Seyed Amir Ahmad Safavi-Naini·Ameneh Salehi·Nariman Naderi·Dorsa Alijanzadeh·Pardis Ketabi Moghadam·Kaveh Kavosi·Negar Golestani·Shabnam Shahrokh·Soltanali Fallah·Jamil S Samaan·Nicholas P. Tatonetti·Nicholas Hoerter·Girish Nadkarni·Hamid Asadzadeh Aghdaei·Ali Soroush
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Abstract

Introduction: This study provides a comprehensive performance assessment of vision-language models (VLMs) against established convolutional neural networks (CNNs) and classic machine learning models (CMLs) for computer-aided detection (CADe) and computer-aided diagnosis (CADx) of colonoscopy polyp images. Method: We analyzed 2,258 colonoscopy images with corresponding pathology reports from 428 patients. We preprocessed all images using standardized techniques (resizing, normalization, and augmentation) and implemented a rigorous comparative framework evaluating 11 distinct models: ResNet50, 4 CMLs (random forest, support vector machine, logistic regression, decision tree), two specialized contrastive vision language encoders (CLIP, BiomedCLIP), and three general-purpose VLMs ( GPT-4 Gemini-1.5-Pro, Claude-3-Opus). Our performance assessment focused on two clinical tasks: polyp detection (CADe) and classification (CADx). Result: In polyp detection, ResNet50 achieved the best performance (F1: 91.35%, AUROC: 0.98), followed by BiomedCLIP (F1: 88.68%, AUROC: [AS1] ). GPT-4 demonstrated comparable effectiveness to traditional machine learning approaches (F1: 81.02%, AUROC: [AS2] ), outperforming other general-purpose VLMs. For polyp classification, performance rankings remained consistent but with lower overall metrics. ResNet50 maintained the highest efficacy (weighted F1: 74.94%), while GPT-4 demonstrated moderate capability (weighted F1: 41.18%), significantly exceeding other VLMs (Claude-3-Opus weighted F1: 25.54%, Gemini 1.5 Pro weighted F1: 6.17%). Conclusion: CNNs remain superior for both CADx and CADe tasks. However, VLMs like BioMedCLIP and GPT-4 may be useful for polyp detection tasks where training CNNs is not feasible.

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Mohammad Amin Khalafi, Seyed Amir Ahmad Safavi-Naini, Ameneh Salehi, Nariman Naderi, Dorsa Alijanzadeh, Pardis Ketabi Moghadam, Kaveh Kavosi, Negar Golestani, Shabnam Shahrokh, Soltanali Fallah, Jamil S Samaan, Nicholas P. Tatonetti, Nicholas Hoerter, Girish Nadkarni, Hamid Asadzadeh Aghdaei, Ali Soroush. 2025-03-27. Vision Language Models versus Machine Learning Models Performance on Polyp Detection and Classification in Colonoscopy Images. https://doi.org/10.1038/s41598-025-29566-2

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