# HOG-CNN: Integrating Histogram of Oriented Gradients with Convolutional Neural Networks for Retinal Image Classification

arXiv: https://arxiv.org/abs/2507.22274 · HTML: https://mlsanity.com/paper/2507.22274/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 56 references that are in the map.
- First posted on arXiv 29 Jul 2025.

## Builds on (most cited first, 13 of 13)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination](https://mlsanity.com/paper/1805.01978/) (2018)
- [Unsupervised Embedding Learning via Invariant and Spreading Instance Feature](https://mlsanity.com/paper/1904.03436/) (2019)
- [Explainable deep learning models in medical image analysis](https://mlsanity.com/paper/2005.13799/) (2020)
- [Applications of Deep Learning in Fundus Images: A Review](https://mlsanity.com/paper/2101.09864/) (2021)
- [Fast and Accurate Tumor Segmentation of Histology Images using Persistent Homology and Deep Convolutional Features](https://mlsanity.com/paper/1805.03699/) (2018)
- [Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence](https://mlsanity.com/paper/2006.07882/) (2020)
- [ADAM Challenge: Detecting Age-related Macular Degeneration from Fundus Images](https://mlsanity.com/paper/2202.07983/) (2022)
- [Functional Summaries of Persistence Diagrams](https://mlsanity.com/paper/1804.01618/) (2018)
- [Blended Multi-Modal Deep ConvNet Features for Diabetic Retinopathy Severity Prediction](https://mlsanity.com/paper/2006.00197/) (2020)
- [An Ensemble Deep Learning Based Approach for Red Lesion Detection in Fundus Images](https://mlsanity.com/paper/1706.03008/) (2017)
- [Topological Data Analysis of Task-Based fMRI Data from Experiments on Schizophrenia](https://mlsanity.com/paper/1809.08504/) (2018)
- [Automated Diabetic Retinopathy Grading using Deep Convolutional Neural Network](https://mlsanity.com/paper/2004.06334/) (2020)

## Built on by (most cited first, 10 of 10)

- [HistoViT: Vision Transformer for Accurate and Scalable Histopathological Cancer Diagnosis](https://mlsanity.com/paper/2508.11181/) (2025)
- [Transfer Learning with EfficientNet for Accurate Leukemia Cell Classification](https://mlsanity.com/paper/2508.06535/) (2025)
- [RepViT-CXR: A Channel Replication Strategy for Vision Transformers in Chest X-ray Tuberculosis and Pneumonia Classification](https://mlsanity.com/paper/2509.08234/) (2025)
- [Efficient Breast and Ovarian Cancer Classification via ViT-Based Preprocessing and Transfer Learning](https://mlsanity.com/paper/2509.18553/) (2025)
- [Colormap-Enhanced Vision Transformers for MRI-Based Multiclass (4-Class) Alzheimer's Disease Classification](https://mlsanity.com/paper/2512.16964/) (2025)
- [Four-Stage Alzheimer's Disease Classification from MRI Using Topological Feature Extraction, Feature Selection, and Ensemble Learning](https://mlsanity.com/paper/2601.00918/) (2026)
- [Hybrid Topological and Deep Feature Fusion for Accurate MRI-Based Alzheimer's Disease Severity Classification](https://mlsanity.com/paper/2602.00956/) (2026)
- [Brain Tumor Classification from 3D MRI Using Persistent Homology and Betti Features: A Topological Data Analysis Approach on BraTS2020](https://mlsanity.com/paper/2603.13771/) (2026)
- [Enhancing Brain Tumor Classification Using Vision Transformers with Colormap-Based Feature Representation on BRISC2025 Dataset](https://mlsanity.com/paper/2603.21234/) (2026)
- [Bridging Topology and Deep Representation Learning: A TDA-ViT Fusion Model for Four-Class Brain Tumor Classification](https://mlsanity.com/paper/2606.00927/) (2026)

## Abstract (arXiv)

The analysis of fundus images is critical for the early detection and diagnosis of retinal diseases such as Diabetic Retinopathy (DR), Glaucoma, and Age-related Macular Degeneration (AMD). Traditional diagnostic workflows, however, often depend on manual interpretation and are both time- and resource-intensive. To address these limitations, we propose an automated and interpretable clinical decision support framework based on a hybrid feature extraction model called HOG-CNN. Our key contribution lies in the integration of handcrafted Histogram of Oriented Gradients (HOG) features with deep convolutional neural network (CNN) representations. This fusion enables our model to capture both local texture patterns and high-level semantic features from retinal fundus images. We evaluated our model on three public benchmark datasets: APTOS 2019 (for binary and multiclass DR classification), ORIGA (for Glaucoma detection), and IC-AMD (for AMD diagnosis); HOG-CNN demonstrates consistently high performance. It achieves 98.5% accuracy and 99.2 AUC for binary DR classification, and 94.2 AUC for five-class DR classification. On the IC-AMD dataset, it attains 92.8% accuracy, 94.8% precision, and 94.5 AUC, outperforming several state-of-the-art models. For Glaucoma detection on ORIGA, our model achieves 83.9% accuracy and 87.2 AUC, showing competitive performance despite dataset limitations. We show, through comprehensive appendix studies, the complementary strength of combining HOG and CNN features. The model's lightweight and interpretable design makes it particularly suitable for deployment in resource-constrained clinical environments. These results position HOG-CNN as a robust and scalable tool for automated retinal disease screening.