• Mukund Sharma
• Harshita Shetty
• Aman Shetty
• Vinay Somani
• Ashraf Siddiqui

The proposed AI image segmentation using the U-Net architecture aims to address the challenge of accurate and efficient medical image analysis and Segmentation. Medical image segmentation involves identifying and delineating specific regions of interest within medical images, such as tumors, major organs, blood vessels, or other anatomical structures. This task is crucial for clinical diagnosis, treatment planning, and monitoring Medical image segmentation is critical in the proper analysis and diagnosis of a wide range of diseases and ailments. This project’s approach to medical picture segmentation that makes use of a "U"-shaped convolutional neural network architecture known as a U-Net. The proposed U-Net architecture takes advantage of its characteristic "U"-shaped design, which includes a contracting path for capturing contextual information and an expansive path for exact localization. This design enables the network to effectively recognize complicated structures and boundaries present in medical pictures by facilitating the extraction of both high-level and low-level elements. This approach might exhibit extraordinary skill in effectively segmenting medical images by utilizing the power of deep learning and neural networks, offering Better, Faster and Accurate results for diagnosis

AI Image Segmentation,U-Net Architecture, Convolutional Neural Networks (CNNs), Computer-Aided Diagnosis (CAD),Medical Imaging Modalities (e.g., MRI, CT, X-ray)

"AI Model for Bio-Medical Image Segmentation using U-Shaped Neural Architecture", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.i232-i240, April-2024, Available :http://www.jetir.org/papers/JETIR2404829.pdf

"AI Model for Bio-Medical Image Segmentation using U-Shaped Neural Architecture", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppi232-i240, April-2024, Available at : http://www.jetir.org/papers/JETIR2404829.pdf