Yes / Burns depth evaluation is a lifesaving task and very challenging that requires objective techniques to accomplish. While the visual assessment is the most commonly used by surgeons, its accuracy reliability ranges between 60 and 80% and subjective that lacks any standard guideline. Currently, the only standard adjunct to clinical evaluation of burn depth is Laser Doppler Imaging (LDI) which measures microcirculation within the dermal tissue, providing the burns potential healing time which correspond to the depth of the injury achieving up to 100% accuracy. However, the use of LDI is limited due to many factors including high affordability and diagnostic costs, its accuracy is affected by movement which makes it difficult to assess paediatric patients, high level of human expertise is required to operate the device, and 100% accuracy possible after 72 h. These shortfalls necessitate the need for objective and affordable technique. Method: In this study, we leverage the use of deep transfer learning technique using two pretrained models ResNet50 and VGG16 for the extraction of image patterns (ResFeat50 and VggFeat16) from a a burn dataset of 2080 RGB images which composed of healthy skin, first degree, second degree and third-degree burns evenly distributed. We then use One-versus-One Support Vector Machines (SVM) for multi-class prediction and was trained using 10-folds cross validation to achieve optimum trade-off between bias and variance. Results: The proposed approach yields maximum prediction accuracy of 95.43% using ResFeat50 and 85.67% using VggFeat16. The average recall, precision and F1-score are 95.50%, 95.50%, 95.50% and 85.75%, 86.25%, 85.75% for both ResFeat50 and VggFeat16 respectively. Conclusion: The proposed pipeline achieved a state-of-the-art prediction accuracy and interestingly indicates that decision can be made in less than a minute whether the injury requires surgical intervention such as skin grafting or not.

The aim of this thesis is to investigate the effects of JPEG2000 compression on texture feature extraction from digitized mammograms. A partially automated computer aided diagnosis system is designed, implemented, and tested for this analysis. The system is tested on a database of 60 digital mammograms obtained from the Digital Database for Screening Mammography at the University of South Florida. Using JPEG2000, the mammograms are compressed at 20 different compression ratios ranging from 17:1 to 10,000:1. Two approaches to texture feature extraction are investigated: (i) region of interest (ROI), which is a bounding box around the segmented mass and (ii) rubber band straightening transform (RBST), which is a band of pixels around the segmented mass transformed to a rectangular strip. The gray tone spatial dependent matrices are computed from the ROI and the RBST for the original uncompressed mammograms as well as each group of compressed images. Feature selection and optimization is achieved via stepwise linear discriminant analysis. The efficacy of the features is measured using receiver operator characteristic (ROC) curves. The efficacy of the texture features obtained from the original mammograms is compared to those of the compressed mammograms. Overall, the texture feature efficacy was preserved even for relatively high compression ratios. For example, the area under the ROC curve was greater than 0.99 for compression ratios as high as 5000:1, when the RBST method was utilized. Overall, the JPEG2000 compression distorted the RBST texture features lesser than the ROI texture features.

No description available.

No description available.

No description available.

No description available.

No description available.

No description available.

No description available.

No description available.