Date of Publication :15th December 2024

Abstract: Medical image-segmentation has become an important in healthcare, advancement in diagnostics, treatment planning, and surgery by identifying key structures of the complex images such as MRI, CT scan, and ultrasound scans. But with the huge volume and variety of medical imaging data, it creates significant difficulties in processing, storage, and analysis of the data and therefore the solutions that solves all these hurdles are eminent. Using deep learning, it will develop methods capable of re al-time large-scale segmentation of medical images. Novel approach which we are putting forward is to deal with high-dimensional image datasets and enhancements on algorithms for rapid feature segmentation across a range of patient’s medical images. Key techni ques to be used in the approach include the ResNet-34 feature encoder, which can extract hierarchical features from images, and then the Dense Atrous Convolution block in order to capture information in multi-scale spatial processes. There would also be a Residual Multi-kernel Pooling block to enable rich contextual understanding. The architecture would end with a feature decoder so that segmented images could clearly be reconstructed. It presents much better speed and accuracy in segmentation across several patients, so it is extremely well-suited for a real-time large application in health care.

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