Novel Approaches of Quantum Computing Based Artificial Intelligence Models in Medical Image Analysis
Abstract
Quantum computing now shapes future research directions. It combines with artificial intelligence methods. This paper reviews several novel studies. It presents and discusses their performance. The studies use quantumenhanced optimization techniques. They also apply hybrid classical-quantum models. Quantum preprocessing methods are analysed here. Medical images need robust preprocessing modules. These support efficient downstream tasks. Modalities like MRI, Fundus, CT and X-ray are covered. Several quantum methods are critiqued. These include the quantum distance measure. They include quantum classical convolutional neural networks. They include quantumbehaved particle swarm optimization. They also include quantum ordinal regression. All show excellent evaluation metrics. They detect diabetic retinopathy and Alzheimer's. They also detect brain tumors and pneumonia. Segmentation methods include SQMLP-Net and quantum clustering. These reach competitive Dice and Jaccard scores. This confirms the strength of quantum approaches. They aid feature extraction and spatial localization. Most models remain at an experimental level. They run on quantum simulated hardware. Still their performance is very promising. Quantum computation may transform medical image processing.
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