Skull Fracture

Skull fractures, breaks in the skull bone often resulting from trauma, are diagnosed primarily using CT scans. Current research focuses on developing automated classification systems using convolutional neural networks (CNNs), often coupled with machine learning algorithms like gradient boosted decision trees or lazy learning approaches, to improve diagnostic accuracy and speed. These CNN-based models achieve high accuracy in classifying different types of skull fractures from CT images, potentially reducing diagnostic delays and improving patient outcomes. This automated analysis offers significant potential for assisting radiologists and streamlining the diagnostic process.

Papers

December 29, 2022

TAToo: Vision-based Joint Tracking of Anatomy and Tool for Skull-base Surgery
Zhaoshuo Li, Hongchao Shu, Ruixing Liang, Anna Goodridge, Manish Sahu, Francis X. Creighton, Russell H. Taylor, Mathias Unberath
BRIO Tool Joint Angle Polyp Phantom Surgical Navigation Skull Base Surgery Skull Fracture

August 14, 2022

Predicting skull fractures via CNN with classification algorithms
Md Moniruzzaman Emon, Tareque Rahman Ornob, Moqsadur Rahman
Convolutional Neural Network Convolutional Neural Network Model Classification Algorithm Synthetic Skull CT Skull Fracture

March 21, 2022

Classifications of Skull Fractures using CT Scan Images via CNN with Lazy Learning Approach
Md Moniruzzaman Emon, Tareque Rahman Ornob, Moqsadur Rahman
Convolutional Neural Network Classification Code CT Image CNN Network Lazy Learning Skull Fracture

Skull Fracture

Papers

TAToo: Vision-based Joint Tracking of Anatomy and Tool for Skull-base Surgery

Predicting skull fractures via CNN with classification algorithms

Classifications of Skull Fractures using CT Scan Images via CNN with Lazy Learning Approach