Abstract
In recent decades, Diabetic Macular Edema (DME) has emerged as a significant cause of vision loss among diabetic patients due to retinal fluid leakage. To address this challenge, reliable and efficient diagnostic methods are essential. The proposed methodology aims to facilitate early detection through a multi-stage process, including feature extraction, feature selection, and classification.For feature extraction, we introduce the H2A2Net model, which incorporates a Dense Spectral-Spatial Module (DSSM) that employs 3D convolutional DenseNet-inspired layers to extract spectral-spatial features. This is complemented by a Hybrid Resolution Module (HRM) designed to achieve fine spatial detail through a multi-scale process. Additionally, a Double Attention Module (DAM) is implemented to capture global and cross-channel interactions, utilizing both pixel-wise and channel-wise attenuation. Feature selection is conducted using Cuckoo Search Spider Monkey Optimization (CSSMO), which effectively processes both local and global searches to enable efficient selection of high-value features. In the classification phase, a hybrid AdaBoost-Backpropagation Neural Network (BPNN) model is employed, where BPNNs function as weak classifiers whose outputs are iteratively boosted to create a strong ensemble. Experimental results on the CUHK dataset demonstrate that the proposed method achieves an accuracy of 97.4%, a recall of 97.6%, a specificity of 97%, and an F1-score of 98%. These outcomes surpass those of existing state-of-the-art methods, indicating that the proposed approach offers enhanced robustness and efficiency for DME classification.
References
Hui, Vivian WK, Simon KH Szeto, Fangyao Tang, Dawei Yang, Haoyu Chen, Timothy YY Lai, Ao Rong et al. "Optical coherence tomography classification systems for diabetic macular edema and their associations with visual outcome and treatment responses–an updated review." Asia-Pacific Journal of Ophthalmology 11, no. 3 (2022): 247-257.
Parodi Battaglia, Maurizio, Pierluigi Iacono, Marialucia Cascavilla, Ilaria Zucchiatti, and Francesco Bandello. "A pathogenetic classification of diabetic macular edema." Ophthalmic Research 60, no. 1 (2018): 23-28.
Arf, Serra, Isil Sayman Muslubas, Mumin Hocaoglu, Mehmet Giray Ersoz, Hakan Ozdemir, and Murat Karacorlu. "Spectral domain optical coherence tomography classification of diabetic macular edema: a new proposal to clinical practice." Graefe's archive for clinical and experimental ophthalmology 258 (2020): 1165-1172.
Ruia, S., Saxena, S., Cheung, C. M. G., Gilhotra, J. S., & Lai, T. Y. (2016). Spectral domain optical coherence tomography features and classification systems for diabetic macular edema: a review. The Asia-Pacific Journal of Ophthalmology, 5(5), 360-367.
Kulyabin, Mikhail, Aleksei Zhdanov, Anastasia Nikiforova, Andrey Stepichev, Anna Kuznetsova, Mikhail Ronkin, Vasilii Borisov et al. "Octdl: Optical coherence tomography dataset for image-based deep learning methods." Scientific data 11, no. 1 (2024): 365.
Akilandeswari, A., T. Jerry Alexander, P. Ganesan, V. Janakiraman, D. Akila, and G. Sajiv. "Superpixels-based Segmentation and Classification using Modified Fuzzy C-Means Clustering and Color Features." In 2024 10th International Conference on Communication and Signal Processing (ICCSP), IEEE, 2024, 1223-1228.
Jampol, Lee M. "Classifications of diabetic macular edema." European Journal of Ophthalmology 30, no. 1 (2020): 6-7.
Garcia-Nonoal, Zaira, Atoany Fierro-Radilla, and Mariko Nakano. "A Comprehensive Approach for an Interpretable Diabetic Macular Edema Grading System Based on ConvUNext." Applied Sciences 14, no. 16 (2024): 7262.
Thanikachalam, V., K. Kabilan, and Sudheer Kumar Erramchetty. "Optimized deep CNN for detection and classification of diabetic retinopathy and diabetic macular edema." BMC Medical Imaging 24, no. 1 (2024): 227.
Fujiwara, Atsushi, Yuki Kanzaki, Shuhei Kimura, Mio Hosokawa, Yusuke Shiode, Shinichiro Doi, Kosuke Takahashi, Ryo Matoba, and Yuki Morizane. "En face image-based classification of diabetic macular edema using swept source optical coherence tomography." Scientific reports 11, no. 1 (2021): 7665.
Im, James HB, Ya-Ping Jin, Ronald Chow, and Peng Yan. "Prevalence of diabetic macular edema based on optical coherence tomography in people with diabetes: A systematic review and meta-analysis." Survey of Ophthalmology 67, no. 4 (2022): 1244-1251.
Liu, Xinle, Tayyeba K. Ali, Preeti Singh, Ami Shah, Scott Mayer McKinney, Paisan Ruamviboonsuk, Angus W. Turner et al. "Deep learning to detect OCT-derived diabetic macular edema from color retinal photographs: a multicenter validation study." Ophthalmology Retina 6, no. 5 (2022): 398-410.
Guo, Xiaoxin, Xinfeng Lu, Baoliang Zhang, Xiaoying Hu, and Songtian Che. "Automatic detection and grading of diabetic macular edema based on a deep neural network." Retina 42, no. 6 (2022): 1095-1102.
Kumar, Amit, Anand Shanker Tewari, and Jyoti Prakash Singh. "Classification of diabetic macular edema severity using deep learning technique." Research on Biomedical Engineering 38, no. 3 (2022): 977-987.
da Costa, Inês Coelho, Sónia Torres-Costa, Guilherme Barbosa, Eduardo Carvalho, Marco Parente, Ana Guerra, Nilza Ramião, and Manuel Falcão. "Deep Learning Network to Distinguish Between Retinal Vein Occlusion and Diabetic Macular Edema." Investigative Ophthalmology & Visual Science 65, no. 7 (2024): 1611-1611.
Hughes-Cano, J. A., Hugo Quiroz-Mercado, L. F. Hernández-Zimbrón, R. García-Franco, JF Rubio Mijangos, E. López-Star, M. García-Roa, V. C. Lansingh, U. Olivares-Pinto, and S. C. Thébault. "Improved predictive diagnosis of diabetic macular edema based on hybrid models: An observational study." Computers in Biology and Medicine 170 (2024): 107979.
Wu, Tianzhu, Liting Liu, Tianer Zhang, and Xuesen Wu. "Deep learning-based risk classification and auxiliary diagnosis of macular edema." Intelligence-Based Medicine 6 (2022): 100053.
Kaymak, Sertan, and Ali Serener. "Automated age-related macular degeneration and diabetic macular edema detection on oct images using deep learning." In 2018 IEEE 14th international conference on intelligent computer communication and processing (ICCP), IEEE, 2018, 265-269.
Shi, Hao, Guo Cao, Youqiang Zhang, Zixian Ge, Yanbo Liu, and Peng Fu. "H2A2Net: A hybrid convolution and hybrid resolution network with double attention for hyperspectral image classification." Remote Sensing 14, no. 17 (2022): 4235.
Ganesamoorthy, B. Narmadha, D. S. Sakthivel, and K. Balasubadra. "Hen maternal care inspired optimization framework for attack detection in wireless smart grid network." Int J Inf Commun Technol (IJ-ICT) 13 (2024): 123-130.
Joshi, Amol Avinash, and Rabia Musheer Aziz. "A two-phase cuckoo search based approach for gene selection and deep learning classification of cancer disease using gene expression data with a novel fitness function." Multimedia Tools and Applications 83, no. 28 (2024): 71721-71752.
Cai, Bingzi, Mutian Li, Huawei Yang, Chunsheng Wang, and Yougen Chen. "State of Charge Estimation of Lithium-Ion Battery Based on Back Propagation Neural Network and AdaBoost Algorithm." Energies 16, no. 23 (2023): 7824.