# An Explorative Analysis of SVM Classifier and ResNet50 Architecture on African Food Classification

arXiv: https://arxiv.org/abs/2505.13923 · HTML: https://mlsanity.com/paper/2505.13923/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 28 references that are in the map.
- First posted on arXiv 20 May 2025.

## Builds on (most cited first, 3 of 3)

- [Metrics for Multi-Class Classification: an Overview](https://mlsanity.com/paper/2008.05756/) (2020)
- [Masked Face Recognition using ResNet-50](https://mlsanity.com/paper/2104.08997/) (2021)
- [CNN and ViT Efficiency Study on Tiny ImageNet and DermaMNIST Datasets](https://mlsanity.com/paper/2505.08259/) (2025)

## Built on by (most cited first, 3 of 3)

- [an interpretable vision transformer framework for automated brain tumor classification](https://mlsanity.com/paper/2604.21311/) (2026)
- [FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis](https://mlsanity.com/paper/2605.05499/) (2026)
- [HADS-Net:A Hybrid Attention-Augmented Dual-Stream Network with Physics-Informed Augmentation for Breast Ultrasound Image Classification](https://mlsanity.com/paper/2605.20536/) (2026)

## Abstract (arXiv)

Food recognition systems has advanced significantly for Western cuisines, yet its application to African foods remains underexplored. This study addresses this gap by evaluating both deep learning and traditional machine learning methods for African food classification. We compared the performance of a fine-tuned ResNet50 model with a Support Vector Machine (SVM) classifier. The dataset comprises 1,658 images across six selected food categories that are known in Africa. To assess model effectiveness, we utilize five key evaluation metrics: Confusion matrix, F1-score, accuracy, recall and precision. Our findings offer valuable insights into the strengths and limitations of both approaches, contributing to the advancement of food recognition for African cuisines.