In order to verify the effectiveness of 3D multimodal medical image segmentation algorithm based on deep reinforcement learning, the algorithm is verified by experiments. The LIDC-IDRI data set, the ...

Deep learning is a subset of machine learning that encompasses a variety of neural network architectures used to perform diverse computer vision tasks such as medical image classification and ...

The experimental results prove the parallel deep learning algorithm with hybrid attention mechanism performed well in image segmentation of lung tumors, and its accuracy can reach 94.61%. Published in ...

The study aims to enhance the accuracy and practicability of CT image segmentation and volume measurement of ICH by using deep ... Ghosh, R., Tanamala, S., Biviji, M., Campeau, N. G., Venugopal, V. K.

The AttendSeg deep learning model performs semantic segmentation at an accuracy that is almost on-par with RefineNet while cutting down the number of parameters to 1.19 million.

Automated methods enable the analysis of PET/CT scans (left) to accurately predict tumor location and size (right). Credit: Nature Machine Intelligence (2024). DOI: 10.1038/s42256-024-00912-9 ...

Hence in this work, a deep learning based algorithm for the automated segmentation and quantification of kidney structures was developed successfully. The proposed work has produced great result and ...

Other vision problems besides basic image classification that have been solved with deep learning include image classification with localization, object detection, object segmentation, image style ...