# Rasheed Omobolaji Alabi

**Type:** Researchers  
**Canonical URL:** https://scholariq.org/researchers/rasheed-omobolaji-alabi/

## Facts

| Field | Value |
| --- | --- |
| Citations | 1,296 |
| Field | Head and Neck Cancer Studies |
| h-index | 15 |
| i10-index | 18 |
| Last Known Institution | University of Helsinki |
| OpenAlex ID | https://openalex.org/A5083399995 |
| ORCID iD | 0000-0001-7655-5924 |
| Works | 42 |

## Researcher papers

- [Machine learning in oral squamous cell carcinoma: Current status, clinical concerns and prospects for future—A systematic review](https://scholariq.org/papers/machine-learning-in-oral-squamous-cell-carcinoma-current-status-clinical/)
- [Machine learning explainability in nasopharyngeal cancer survival using LIME and SHAP](https://scholariq.org/papers/machine-learning-explainability-in-nasopharyngeal-cancer-survival-using-lime-and/)
- [Comparison of supervised machine learning classification techniques in prediction of locoregional recurrences in early oral tongue cancer](https://scholariq.org/papers/comparison-of-supervised-machine-learning-classification-techniques-in/)
- [Machine learning application for prediction of locoregional recurrences in early oral tongue cancer: a Web-based prognostic tool](https://scholariq.org/papers/machine-learning-application-for-prediction-of-locoregional-recurrences-in-early/)
- [Comparison of nomogram with machine learning techniques for prediction of overall survival in patients with tongue cancer](https://scholariq.org/papers/comparison-of-nomogram-with-machine-learning-techniques-for-prediction-of/)
- [Deep Machine Learning for Oral Cancer: From Precise Diagnosis to Precision Medicine](https://scholariq.org/papers/deep-machine-learning-for-oral-cancer-from-precise-diagnosis-to-precision/)
- [Clinical significance of tumor-stroma ratio in head and neck cancer: a systematic review and meta-analysis](https://scholariq.org/papers/clinical-significance-of-tumor-stroma-ratio-in-head-and-neck-cancer-a-systematic/)
- [Artificial Intelligence in Head and Neck Cancer: A Systematic Review of Systematic Reviews](https://scholariq.org/papers/artificial-intelligence-in-head-and-neck-cancer-a-systematic-review-of/)
- [An interpretable machine learning prognostic system for risk stratification in oropharyngeal cancer](https://scholariq.org/papers/an-interpretable-machine-learning-prognostic-system-for-risk-stratification-in/)
- [Artificial Intelligence-Driven Radiomics in Head and Neck Cancer: Current Status and Future Prospects](https://scholariq.org/papers/artificial-intelligence-driven-radiomics-in-head-and-neck-cancer-current-status/)
- [Measuring the Usability and Quality of Explanations of a Machine Learning Web-Based Tool for Oral Tongue Cancer Prognostication](https://scholariq.org/papers/measuring-the-usability-and-quality-of-explanations-of-a-machine-learning-web/)

## Researcher topics

- [Head and Neck Cancer Studies](https://scholariq.org/topics/head-and-neck-cancer-studies/)
- [Radiomics and Machine Learning in Medical Imaging](https://scholariq.org/topics/radiomics-and-machine-learning-in-medical-imaging/)
- [AI in cancer detection](https://scholariq.org/topics/ai-in-cancer-detection/)
- [Artificial Intelligence in Healthcare and Education](https://scholariq.org/topics/artificial-intelligence-in-healthcare-and-education/)
- [Oral Health Pathology and Treatment](https://scholariq.org/topics/oral-health-pathology-and-treatment/)

## Researcher university

- [University of Helsinki](https://scholariq.org/institutions/university-of-helsinki/)

---
Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.