Deep Learning Applications in Medical Image Analysis: U-Net for Diagnosis
Keywords:
medical image analysis, U-Net, segmentation, deep learning, diagnostic accuracy.Abstract
This research investigates the application of U-Net engineering in restorative image investigation for enhanced symptomatic capabilities. Leveraging a different dataset comprising MRI, CT scans, and X-rays, we methodically compare U-Net with conventional CNN, SegNet, and state-of-the-art DeepLabv3. The U-Net show showcases predominant execution, accomplishing a Dice coefficient of 0.85, an Intersection over Union (IoU) of 0.75, and a pixel exactness of 0.92. The incorporation of skip associations in U-Net demonstrates instrumental in protecting spatial data, driving more exact division comes about. Moreover, our examination amplifies to particular therapeutic conditions, illustrating U-Net's flexibility with a Dice coefficient of 0.87 for tumor division and 0.83 for organ outline. The results confirm U-Net as a vigorous and dependable instrument for exact medical picture division, with suggestions for improved demonstrative precision over different imaging modalities.
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