3D Neural Network for Detection of ACL Injury in Knee MRI Scans / (Record no. 608905)
[ view plain ]
| 000 -LEADER | |
|---|---|
| fixed length control field | 02096nam a22001577a 4500 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 610 |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Kamran, Abdullah |
| 245 ## - TITLE STATEMENT | |
| Title | 3D Neural Network for Detection of ACL Injury in Knee MRI Scans / |
| Statement of responsibility, etc. | Abdullah Kamran |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | Islamabad : |
| Name of producer, publisher, distributor, manufacturer | SMME- NUST; |
| Date of production, publication, distribution, manufacture, or copyright notice | 2022. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 43p. |
| Other physical details | Soft Copy |
| Dimensions | 30cm |
| 500 ## - GENERAL NOTE | |
| General note | Computer aided diagnosis is widely used in medical imaging for the diagnosis of many<br/>diseases such as cardiomegaly, brain and kidney tumor, lung cancer, COVID-19 and<br/>may more. For the past few decades, computer aided diagnosis has significantly<br/>improved due to the development of better architecture used for the diagnosis. Knee<br/>injury diagnosis using deep learning techniques is highly popular due its high detection<br/>rate and is highly localized. Many state-of-the-art-deep learning models have been<br/>used for the detection of abnormalities, meniscus tear and ACL tears in Knee MRI<br/>scans. These models include RESNET, Google-Net, VGG19 and VGG16, Alex-Net<br/>and many other, all giving significant results. In this study we used a custom 3D CNN<br/>model which is light in weight. For training we are using two datasets, one provided<br/>by Stanford ML group and the other form Hospital in Croatia. We combined the two<br/>dataset and split it into 80-20 ration (80% of the data used for training and remaining<br/>for testing purposes). Both the dataset has extreme class imbalance, so we used data<br/>augmentation and class weights to rectify its effect on the training process. Further the<br/>voxel intensities for the two datasets were different (one dataset was in 8-bit format<br/>and the second was in 12-bit format), so we normalized the intensity values using<br/>mathematical formulas. For contrast, we performed adaptive histogram equalization<br/>Average accuracy and AUC achieved by our model on training set is 97.6 and 99.3<br/>respectively, during 5-fold cross validation. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | MS Biomedical Engineering (BME) |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Supervisor : Dr. Omer Gilani |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="http://10.250.8.41:8080/xmlui/handle/123456789/31042">http://10.250.8.41:8080/xmlui/handle/123456789/31042</a> |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | |
| Koha item type | Thesis |
| Withdrawn status | Permanent Location | Current Location | Shelving location | Date acquired | Full call number | Barcode | Koha item type |
|---|---|---|---|---|---|---|---|
| School of Mechanical & Manufacturing Engineering (SMME) | School of Mechanical & Manufacturing Engineering (SMME) | E-Books | 04/22/2024 | 610 | SMME-TH-772 | Thesis |