Deep learning for automated diagnosis of uveal melanoma

Albert K. Dadzie; Sabrina P. Iddir; Mansour Abtahi; Behrouz Ebrahimi; David Le; Taeyoon Son; Michael J. Heiferman; Xincheng Yao

doi:10.1117/12.3000655

12 March 2024 Deep learning for automated diagnosis of uveal melanoma

Albert K. Dadzie, Sabrina P. Iddir, Mansour Abtahi, Behrouz Ebrahimi, David Le, Taeyoon Son, Michael J. Heiferman, Xincheng Yao

Author Affiliations +

Proceedings Volume 12824, Ophthalmic Technologies XXXIV; 128240B (2024) https://doi.org/10.1117/12.3000655
Event: SPIE BiOS, 2024, San Francisco, California, United States

Abstract

Accurate differentiation of uveal melanoma and choroidal nevi is critical for optimal patient care, preventing unnecessary procedures for benign lesions while ensuring timely intervention for potentially malignant cases. This study aimed to validate deep learning classification of these lesions and to evaluate the impact of different color fusion options on classification performance. To evaluate the effect of color fusion options on the classification performance, we tested early fusion, intermediate fusion, and late fusion using ultra-widefield retinal images. Specificity, sensitivity, F1-score, accuracy, and the area under the curve (AUC) of a receiver operating characteristic (ROC) were used to assess the performance of the deep learning model. The results show that the color fusion options significantly impacted the deep learning classification performance, with intermediate fusion emerging as the best strategy, outperforming both single-color learning and the other fusion strategies. The intermediate fusion strategy had an accuracy of 89.72%, sensitivity of 85.05%, specificity of 91.64, F1 score of 0.8492 and an AUC of 0.9335. These compelling results emphasize the vast potential of deep learning to enhance the accuracy of diagnosis and classification of UM and choroidal nevi, leading to improved patient outcomes and optimized treatment strategies. By harnessing the power of deep learning and color fusion strategies, this study not only provides valuable insights into the application of these approaches in the field of ophthalmology but also highlights their critical significance in automating the classification of UM and choroidal nevi.

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Citation Download Citation

Albert K. Dadzie, Sabrina P. Iddir, Mansour Abtahi, Behrouz Ebrahimi, David Le, Taeyoon Son, Michael J. Heiferman, and Xincheng Yao "Deep learning for automated diagnosis of uveal melanoma", Proc. SPIE 12824, Ophthalmic Technologies XXXIV, 128240B (12 March 2024); https://doi.org/10.1117/12.3000655

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