Abstract
Automatic classification of femur trochanteric fracture is very valuable in clinical diagnosis practice. However, developing a high classification performance system is still challenging due to the various locations, shapes, and contextual information of the fracture regions. To tackle this challenge, we propose a novel dense dilated attentive (DDA) network for more accurate classification of 31A1/31A2/31A3 fractures from the X-ray images by incorporating a DDA layer. By exploiting this layer, the multiscale, contextual, and attentive features are encoded from different depths of the network and thus improving the feature learning ability of the classification network to gain a better classification performance. To validate the effectiveness of the DDA network, we conduct extensive experiments on the annotated femur trochanteric fracture data samples, and the experimental results demonstrate that the proposed DDA network could achieve competitive classification compared with other methods.
Highlights
Femur trochanteric fracture is one of the most commonly occurred fractures among elderly people
To validate the effectiveness of dilated attention (DDA) network, we perform extensive experiments on the femur trochanteric fracture images, and the experimental results show that our proposed DDA network could efficiently improve the classification performance by successfully extracting the discriminative features from the input image
To evaluate the performance of the proposed DDA network, we apply four evaluation metrics; here, we denote the true positive, false positive, true negative, and false negative as TP, FP, TN, and FN. en, the accuracy which calculates the correct prediction among the total numbers of samples could be calculated as accuracy
Summary
Femur trochanteric fracture is one of the most commonly occurred fractures among elderly people. With the rapid growth of the aging population worldwide, the occurrence of this fracture increases rapidly which severely threatens the health of elderly people Since this fracture could lead to high mortality rates and dramatically affect the quality of patients’ life, effective and timely treatment is essential to relieve the pain of patients during the clinical diagnosis. The conventional diagnosis method inspects patient images slice by slice which is usually tedious and time-consuming for the radiologists, and with different clinical experiences of radiologists, the final diagnosis result is liable to be empirical and subjective, which may hamper making the follow-up treatment plan To tackle this challenge, a practicable way is to design a fracture computer-aided system [2,3,4,5,6] that helps the radiologist classify the fracture types automatically. Those methods have achieved in classifying the fracture task; those have deficiencies in capturing the robust and high-level semantic features due to hand-crafted feature predefinition
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
