Medical imaging plays a big role in catching diseases early. Traditional methods like MRI, CT scans, ultrasounds, and PET scans show us the insides of our bodies in 3D, but they often miss little details that could be crucial. This is where holography microscopic imaging comes in handy. It doesn't just capture how bright things are (amplitude), but also their structure (phase). This can provide a more detailed look at biological structures.

Scientists are now exploring how to combine this holography technique with something called Deep Learning (DL) to improve cancer diagnosis. By looking at existing research, they found that DL and holography together could fill some gaps left by traditional imaging methods. This combination can help doctors see more details, leading to more accurate diagnostics.

The process starts with collecting tissue samples and scanning them with holographic imaging. The images are then prepped, especially if they're not balanced, and fed into a DL system using models like U-Net and Vision Transformer (ViT). To make things even better, scientists suggest using Explainable AI (XAI) to help make sense of the data.

But it's not all smooth sailing. There are challenges to this new approach. The study also looks at these issues, offering a balance of advantages and drawbacks. With this kind of thorough research, the future of cancer diagnosis looks promising.

Revamping Cancer Detection: How Holograms and AI Team Up

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