Deep Learning Applications in Medical Image Analysis: U-Net for Diagnosis - Shanthi H J, Hamza Mohammed Ridha Al-Khafaji, Salwan Mohammed Shaheed, Rajasekhar Pittala, Harendra Singh Negi, Arun Pratap Srivastava, Navneet Kumar, Anurag Shrivastava (International Journal of Intelligent Systems and Applications in Engineering, 2024)
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.