Incorporating the Laplacian Filter with a Three-Stream Multi-Channel Convolutional Neural Network for Improved Abnormality Detection in Knee MRIs
Kumar, R.; Bhansali, R. M.
Show abstract
AO_SCPLOWBSTRACTC_SCPLOWDespite ACL and meniscus tears being among the most common movement induced injuries, they are often the most difficult to diagnose due to the variable severity with which these tears occur. Typically, magnetic resonance imaging (MRI) scans are used for diagnosing ligament tears, but performing and analyzing these scans is time consuming and expensive due to the necessitation of a radiologist or professional orthopedic specialist. Consequently, we developed a custom three-stream convolutional neural network (CNN) architecture that contains multiple channels to automate the diagnosis of ACL and meniscus tears from MRI scans. Our algorithm utilizes the sagittal, coronal, and axial slices to maximize feature extraction. Furthermore, we apply the Laplace Operator on the MRI scan images to evaluate and compare its propensity in different medical imaging modalities. The algorithm attained an accuracy of 92.80%, significantly higher than that of orthopedic diagnosis accuracy. Our results point towards the feasibility of shallow, multi-channel CNNs and the ability of the Laplace Operator to improve performance metrics for MRI scan diagnosis.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Classification of Hyper-scale Multimodal Imaging Datasets 94%
- Uncovering the effects of model initialization on deep model generalization: A study with adult and pediatric chest X-ray images 91%
- Implementation and prospective real-time evaluation of a generalized system for in-clinic deployment and validation of machine learning models in radiology 91%
Similar papers in this journal
- An Automated and Robust Tool for Musculoskeletal and Finite Element Modeling of the Knee Joint 95%
- A Hybrid Method for Ultrasound-Based Tracking of Skeletal Muscle Architecture 94%
- Objective Assessment of Beat Quality in Transcranial Doppler Measurement of Blood Flow Velocity in Cerebral Arteries 91%
Similar papers in this journal
- PRCnet: An Efficient Model for Automatic Detection of Brain Tumor in MRI Images 94%
- pyKNEEr: An image analysis workflow for open and reproducible research on femoral knee cartilage 93%
- Enhancing Semantic Segmentation in Chest X-Ray Images through Image Preprocessing: ps-KDE for Pixel-wise Substitution by Kernel Density Estimation 93%
Similar papers in this journal
- Radius-Optimized Efficient Template Matching for Lesion Detection from Brain Images 93%
- Segmentation of Pancreatic Ductal Adenocarcinoma (PDAC) and surrounding vessels in CT images using deep convolutional neural networks and Texture Descriptors 93%
- Estimation of Three-Dimensional Chromatin Morphology for Nuclear Classification and Characterisation 92%
Similar papers in this journal
- Deep learning ensemble for abdominal aortic calcification scoring from lumbar spine X-ray and DXA images 93%
- Fusion of Electronic Health Records and Radiographic Images for a Multimodal Deep Learning Prediction Model of Atypical Femur Fractures 92%
- Two-Step Machine Learning to Diagnose and Predict Involvement of Lungs in COVID-19 and Pneumonia using CT Radiomics 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.