Interactive Effect of Learning Rate and Batch Size to Implement Transfer Learning for Brain Tumor Classification

For classifying brain tumors with small datasets, the knowledge-based transfer learning (KBTL) approach has performed very well in attaining an optimized classification model. However, its successful implementation is typically affected by different hyperparameters, specifically the learning rate (LR), batch size (BS), and their joint influence. In general, most of the existing research could not achieve the desired performance because the work addressed only one hyperparameter tuning. This study adopted a Cartesian product matrix-based approach, to interpret the effect of both hyperparameters and their interaction on the performance of models. To evaluate their impact, 56 two-tuple hyperparameters from the Cartesian product matrix were used as inputs to perform an extensive exercise, comprising 504 simulations for three cutting-edge architecture-based pre-trained Deep Learning (DL) models, ResNet18, ResNet50, and ResNet101. Additionally, the impact was also assessed by using three well-known optimizers (solvers): SGDM, Adam, and RMSProp. The performance assessment showed that the framework is an efficient framework to attain optimal values of two important hyperparameters (LR and BS) and consequently an optimized model with an accuracy of 99.56%. Further, our results showed that both hyperparameters have a significant impact individually as well as interactively, with a trade-off in between. Further, the evaluation space was extended by using the statistical ANOVA analysis to validate the main findings. F-test returned with p < 0.05, confirming that both hyperparameters not only have a significant impact on the model performance independently, but that there exists an interaction between the hyperparameters for a combination of their levels

Mandate of establishing an ethical review committee

The National Bioethics Committee (NBC) was approved by the Government of Pakistan in 2004. Its basic mandate is to “promote and facilitate ethical health services delivery and health related research. The secretariat of NBC is the Pakistan Medical Research Council (PMRC) under Ministry of Health Government of Pakistan. A Code of Ethics has been issued by Pakistan Medical and Dental Council (PMDC) and according to this code all research protocols must be reviewed by an Ethical Review Committee (ERC) at their local institute. A 15- member committee which comprises of faculty from basic sciences, representatives from social science departments, clinicians, a person with knowledge of ethics of medical research and a lay person is an ideal team. This is essential to cater to the well being of patients and safety of research. In this way the importance of ethics will be realized by all health providers. The ERC should inculcate the sense of ethics in health providers and must be realized as an important component of any institutio

Association of Polycystic Ovaries in Primary Infertile Women with Ovarian and Uterine Morphology

Background: To measure the effect of age on ovarian and uterine morphology in infertile women with polycystic ovaries (PCO)Methods: In this observational cross sectional study 100 primary infertile women with PCOs (confirmed by ultrasonography) were subdivided into two age groups. Group-I included between age groups 20-30 years and Group II ranged from 31- 40 years. The ovarian volume (OV), follicles count (FC) and size (FS), uterine area (UA) and endometrial thickness (Endo) were determined by ultrasonography.Results: The ovarian morphology showed a significant decrease in group II; the OV transabdominally was 14.26± 2.42 vs 10.56±1.0 (0.001) and transvaginally 14.93±2.55 vs 11.75±1.06 (0.001). The FC was 14.00±1.60 vs 12.3±0.78 (0.023) and FS was 8.23±8.9vs 3.29±6.9 (0.001). The uterine morphology however showed a significant increase in group II. The UA was 88.79±5.83 vs 119.71±22.83 (0.001) and endo was 0.56±0.10 vs 0.67±0.12 (0.001)Conclusion: The ovarian morphology variables which include OV, FC and FS decreases in the older infertile group of women with PCOs but the uterine morphology variables showed an increase in area with thickening of endometrium in the older group

Interactive Effect of Learning Rate and Batch Size to Implement Transfer Learning for Brain Tumor Classification

For classifying brain tumors with small datasets, the knowledge-based transfer learning (KBTL) approach has performed very well in attaining an optimized classification model. However, its successful implementation is typically affected by different hyperparameters, specifically the learning rate (LR), batch size (BS), and their joint influence. In general, most of the existing research could not achieve the desired performance because the work addressed only one hyperparameter tuning. This study adopted a Cartesian product matrix-based approach, to interpret the effect of both hyperparameters and their interaction on the performance of models. To evaluate their impact, 56 two-tuple hyperparameters from the Cartesian product matrix were used as inputs to perform an extensive exercise, comprising 504 simulations for three cutting-edge architecture-based pre-trained Deep Learning (DL) models, ResNet18, ResNet50, and ResNet101. Additionally, the impact was also assessed by using three well-known optimizers (solvers): SGDM, Adam, and RMSProp. The performance assessment showed that the framework is an efficient framework to attain optimal values of two important hyperparameters (LR and BS) and consequently an optimized model with an accuracy of 99.56%. Further, our results showed that both hyperparameters have a significant impact individually as well as interactively, with a trade-off in between. Further, the evaluation space was extended by using the statistical ANOVA analysis to validate the main findings. F-test returned with p < 0.05, confirming that both hyperparameters not only have a significant impact on the model performance independently, but that there exists an interaction between the hyperparameters for a combination of their levels