Radiation Resilience of Homo and Hetero Based Junctionless Tunnel Field Effect Transistor Devices and Circuits

Abstract

Anterior Cruciate Ligament (ACL) tears are often sustained by football players, volleyball players, sprinters, runners, and other athletes. This typically occurs due to excessive stretching or sudden, abrupt movements, causing intense pain for the individual. While numerous com- puter vision-based techniques have been employed to detect ACL tears, the complex structure of knee ligaments presents significant challenges to the performance of most systems.A novel multidirectional multi-neighbor local binary pattern (MNLBP) texture descriptor is presented for the detection of ACL normal and tear MR image of knee ligament in the initial phase of the study. KNN and SVM classifiers are used to assess the performance of MNLBP. The MRNet knee MR image dataset is used for training as well as testing the system. The performance measure of this methodology is achieved with the help of sensitivity, specificity, accuracy, and the F1 score. The suggested MNLBP performed better than conventional LBP for the KNN and SVM classifiers, achieving a specificity of 0.92 and 0.92, a sensitivity of 0.87 and 0.88 and a precision of 88. 92% and 89. 41%. The second phase of research proposed an effective and simple approach for detecting ACL tears using a convolutional neural network (ATD-CNN) and Meniscal tear to minimize the complexity of the network. Data augmentation based on shifting and rotation is used to develop the synthetic database to diminish the data scarcity is- sue. For the original and supplemented datasets, ATD-CNN yields an accuracy of 90. 10% and newline93. 93%, respectively. The third research phase is to improve the feature uniqueness of knee MR images for the detection of Anterior Cruciate Ligament tears and meniscal tears using a three-layered MultiKernNet DCNN (MultiKernNet). Performance of the proposed methods are assessed with Precision, Recall, Accuracy, and F1-score. Overall Accuracy of the Multi- KernNet is of 96.60%, precision of 0.9654, recall rate of 0.9668, and F1-score of 0.9582. The proposed ATD-CNN and MultiKern