Open Access

ARTICLE

HCSP-Net: A Novel Model of Age-Related Macular Degeneration Classification Based on Color Fundus Photography

by Cheng Wan¹, Jiani Zhao¹, Xiangqian Hong², Weihua Yang^2,*, Shaochong Zhang^2,*

1 College of Electronic and Information Engineering/College of Integrated Circuits, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China
2 Shenzhen Eye Institute, Shenzhen Eye Hospital, Jinan University, Shenzhen, 518040, China

* Corresponding Authors: Weihua Yang. Email: ; Shaochong Zhang. Email:

(This article belongs to the Special Issue: Deep Learning in Computer-Aided Diagnosis Based on Medical Image)

Computers, Materials & Continua 2024, 79(1), 391-407. https://doi.org/10.32604/cmc.2024.048307

Received 04 December 2023; Accepted 14 February 2024; Issue published 25 April 2024

Abstract

Age-related macular degeneration (AMD) ranks third among the most common causes of blindness. As the most conventional and direct method for identifying AMD, color fundus photography has become prominent owing to its consistency, ease of use, and good quality in extensive clinical practice. In this study, a convolutional neural network (CSPDarknet53) was combined with a transformer to construct a new hybrid model, HCSP-Net. This hybrid model was employed to tri-classify color fundus photography into the normal macula (NM), dry macular degeneration (DMD), and wet macular degeneration (WMD) based on clinical classification manifestations, thus identifying and resolving AMD as early as possible with color fundus photography. To further enhance the performance of this model, grouped convolution was introduced in this study without significantly increasing the number of parameters. HCSP-Net was validated using an independent test set. The average precision of HCSP-Net in the diagnosis of AMD was 99.2%, the recall rate was 98.2%, the F1-Score was 98.7%, the PPV (positive predictive value) was 99.2%, and the NPV (negative predictive value) was 99.6%. Moreover, a knowledge distillation approach was also adopted to develop a lightweight student network (SCSP-Net). The experimental results revealed a noteworthy enhancement in the accuracy of SCSP-Net, rising from 94% to 97%, while remarkably reducing the parameter count to a quarter of HCSP-Net. This attribute positions SCSP-Net as a highly suitable candidate for the deployment of resource-constrained devices, which may provide ophthalmologists with an efficient tool for diagnosing AMD.

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Keywords

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