Amalgamation of Artificial Intelligence with Nanoscience for Biomedical Applications

Kasture, Kaustubh; Shende, Pravin

doi:10.1007/s11831-023-09948-3

Cited by 9 publications

(1 citation statement)

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“…There are many challenges to be addressed in the future development of artificial retinas [12,13]. Some of the challenges that can be addressed using artificial intelligence (AI) [14] are improving image resolution and clarity, filtering out unnecessary objects, enhancing visual perception to overcome the limitation of an artificial retina's resolution, and developing better integration of image processing algorithms to mimic the functionality of a human retina. Overall, the primary challenge is to produce such an enhanced image such that not only are the unnecessary objects removed, but vision is not compromised.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Image Enhancement: Feature Engineering for Improved Classification in AI-Assisted Artificial Retinas

Mehmood,

Ko,

Kim

et al. 2024

Sensors

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Artificial retinas have revolutionized the lives of many blind people by enabling their ability to perceive vision via an implanted chip. Despite significant advancements, there are some limitations that cannot be ignored. Presenting all objects captured in a scene makes their identification difficult. Addressing this limitation is necessary because the artificial retina can utilize a very limited number of pixels to represent vision information. This problem in a multi-object scenario can be mitigated by enhancing images such that only the major objects are considered to be shown in vision. Although simple techniques like edge detection are used, they fall short in representing identifiable objects in complex scenarios, suggesting the idea of integrating primary object edges. To support this idea, the proposed classification model aims at identifying the primary objects based on a suggested set of selective features. The proposed classification model can then be equipped into the artificial retina system for filtering multiple primary objects to enhance vision. The suitability of handling multi-objects enables the system to cope with real-world complex scenarios. The proposed classification model is based on a multi-label deep neural network, specifically designed to leverage from the selective feature set. Initially, the enhanced images proposed in this research are compared with the ones that utilize an edge detection technique for single, dual, and multi-object images. These enhancements are also verified through an intensity profile analysis. Subsequently, the proposed classification model’s performance is evaluated to show the significance of utilizing the suggested features. This includes evaluating the model’s ability to correctly classify the top five, four, three, two, and one object(s), with respective accuracies of up to 84.8%, 85.2%, 86.8%, 91.8%, and 96.4%. Several comparisons such as training/validation loss and accuracies, precision, recall, specificity, and area under a curve indicate reliable results. Based on the overall evaluation of this study, it is concluded that using the suggested set of selective features not only improves the classification model’s performance, but aligns with the specific problem to address the challenge of correctly identifying objects in multi-object scenarios. Therefore, the proposed classification model designed on the basis of selective features is considered to be a very useful tool in supporting the idea of optimizing image enhancement.

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Section: Introductionmentioning

confidence: 99%