Simulated multimodal deep facial diagnosis

• DOI: 10.1016/j.eswa.2024.123881
• Publication Date: 2024-04-04T04:22:37Z
• Authors: Bo Jin; Nuno Gonçalves; Leandro Cruz; Iurii Medvedev; Yuanyu Yu; Jiujiang Wang
• Keywords: Leverage (statistics); RGB color model

Facial phenotypes are extensively studied in medical and biological research, serving as critical markers that potentially indicate underlying genetic traits or conditions. With the recent advancements big data, algorithms, hardware, deep facial diagnosis, which employs learning techniques to systematically examine identify signs of certain diseases conditions, has attracted significant attention gradually emerging a promising tool precision medicine. Primarily limited by scarcity data for training diagnosis models, accuracy various conditions remains low up now. In past decade, RGB-D cameras, measuring depth information along with standard RGB capabilities, have proven superior processing spatial details more stability accuracy. Motivated facts mentioned above, this paper, we propose Simulated Multimodal Framework, effectively improves computer-aided performance state-of-the-art models experiments under different The principle is leverage simulated generative improve image recognition. Furthermore, rapid non-invasive disease screening detection, our proposal demonstrated an improvement over 20% compared practicing physicians study.