Automated grading of colon biopsy images across all magnifications is challenging because of tailored segmentation and dependent features on each magnification.This work presents a novel approach of robust magnificati...Automated grading of colon biopsy images across all magnifications is challenging because of tailored segmentation and dependent features on each magnification.This work presents a novel approach of robust magnification-independent colon cancer grading framework to distinguish colon biopsy images into four classes:normal,well,moderate,and poor.The contribution of this research is to develop a magnification invariant hybrid feature set comprising cartoon feature,Gabor wavelet,wavelet moments,HSV histogram,color auto-correlogram,color moments,and morphological features that can be used to characterize different grades.Besides,the classifier is modeled as a multiclass structure with six binary class Bayesian optimized random forest(BO-RF)classifiers.This study uses four datasets(two collected from Indian hospitals—Ishita Pathology Center(IPC)of 4X,10X,and 40X and Aster Medcity(AMC)of 10X,20X,and 40X—two benchmark datasets—gland segmentation(GlaS)of 20X and IMEDIATREAT of 10X)comprising multiple microscope magnifications.Experimental results demonstrate that the proposed method outperforms the other methods used for colon cancer grading in terms of accuracy(97.25%-IPC,94.40%-AMC,97.58%-GlaS,99.16%-Imediatreat),sensitivity(0.9725-IPC,0.9440-AMC,0.9807-GlaS,0.9923-Imediatreat),specificity(0.9908-IPC,0.9813-AMC,0.9907-GlaS,0.9971-Imediatreat)and F-score(0.9725-IPC,0.9441-AMC,0.9780-GlaS,0.9923-Imediatreat).The generalizability of the model to any magnified input image is validated by training in one dataset and testing in another dataset,highlighting strong concordance in multiclass classification and evidencing its effective use in the first level of automatic biopsy grading and second opinion.展开更多
Multilevel unified power flow controllers(ML-UPFCs)aim to improve grid stability,power quality,and fault management.This approach is particularly beneficial for renewable energy systems connected to a grid,where effic...Multilevel unified power flow controllers(ML-UPFCs)aim to improve grid stability,power quality,and fault management.This approach is particularly beneficial for renewable energy systems connected to a grid,where efficient power flow and robust fault handling are crucial for maintaining system reliability.However,current grid-integrated systems face challenges such as inefficient fault management,harmonic distortions,and instability when dealing with nonlinear loads.Existing control strategies often lack the flexibility and optimization required to handle these issues effectively in dynamic grid environments.Therefore,the proposed methodology involves a multistep control strategy to optimize the integration of solar photovoltaic(SPV)systems with MLUPFCs.Initially,the SPV array generates direct current(DC)power,which is optimized using a perturb and observe maximum power point tracking controller.The DC-to-DC boost converter then steps up the voltage for input to a voltage source inverter(VSI)or voltage source converter(VSC).The VSI/VSC,enhanced by greedy control-based monarch butterfly optimization,converts DC to AC while minimizing harmonic distortion.The power is then fed into the grid,which supplies sensitive critical and nonlinear loads.Three-phase fault detection mechanisms and series transformers manage the power flow and fault conditions.Furthermore,the ML-UPFC,controlled by a random forest cuckoo search optimization algorithm,enhances the fault ride-through capabilities and power regulation.Additional transformers and a shunt transformer optimize the voltage levels and reactive power management,ensuring stable and high-quality power delivery to both sensitive and nonlinear loads.Finally,the proposed approach addresses power flow optimization,fault mitigation,and nonlinear load management with the aim of enhancing grid stability and efficiency.展开更多
基金This work was partially supported by the Research Groups Program(Research Group Number RG-1439-033),under the Deanship of Scientific Research,King Saud University,Riyadh,Saudi Arabia.
文摘Automated grading of colon biopsy images across all magnifications is challenging because of tailored segmentation and dependent features on each magnification.This work presents a novel approach of robust magnification-independent colon cancer grading framework to distinguish colon biopsy images into four classes:normal,well,moderate,and poor.The contribution of this research is to develop a magnification invariant hybrid feature set comprising cartoon feature,Gabor wavelet,wavelet moments,HSV histogram,color auto-correlogram,color moments,and morphological features that can be used to characterize different grades.Besides,the classifier is modeled as a multiclass structure with six binary class Bayesian optimized random forest(BO-RF)classifiers.This study uses four datasets(two collected from Indian hospitals—Ishita Pathology Center(IPC)of 4X,10X,and 40X and Aster Medcity(AMC)of 10X,20X,and 40X—two benchmark datasets—gland segmentation(GlaS)of 20X and IMEDIATREAT of 10X)comprising multiple microscope magnifications.Experimental results demonstrate that the proposed method outperforms the other methods used for colon cancer grading in terms of accuracy(97.25%-IPC,94.40%-AMC,97.58%-GlaS,99.16%-Imediatreat),sensitivity(0.9725-IPC,0.9440-AMC,0.9807-GlaS,0.9923-Imediatreat),specificity(0.9908-IPC,0.9813-AMC,0.9907-GlaS,0.9971-Imediatreat)and F-score(0.9725-IPC,0.9441-AMC,0.9780-GlaS,0.9923-Imediatreat).The generalizability of the model to any magnified input image is validated by training in one dataset and testing in another dataset,highlighting strong concordance in multiclass classification and evidencing its effective use in the first level of automatic biopsy grading and second opinion.
文摘Multilevel unified power flow controllers(ML-UPFCs)aim to improve grid stability,power quality,and fault management.This approach is particularly beneficial for renewable energy systems connected to a grid,where efficient power flow and robust fault handling are crucial for maintaining system reliability.However,current grid-integrated systems face challenges such as inefficient fault management,harmonic distortions,and instability when dealing with nonlinear loads.Existing control strategies often lack the flexibility and optimization required to handle these issues effectively in dynamic grid environments.Therefore,the proposed methodology involves a multistep control strategy to optimize the integration of solar photovoltaic(SPV)systems with MLUPFCs.Initially,the SPV array generates direct current(DC)power,which is optimized using a perturb and observe maximum power point tracking controller.The DC-to-DC boost converter then steps up the voltage for input to a voltage source inverter(VSI)or voltage source converter(VSC).The VSI/VSC,enhanced by greedy control-based monarch butterfly optimization,converts DC to AC while minimizing harmonic distortion.The power is then fed into the grid,which supplies sensitive critical and nonlinear loads.Three-phase fault detection mechanisms and series transformers manage the power flow and fault conditions.Furthermore,the ML-UPFC,controlled by a random forest cuckoo search optimization algorithm,enhances the fault ride-through capabilities and power regulation.Additional transformers and a shunt transformer optimize the voltage levels and reactive power management,ensuring stable and high-quality power delivery to both sensitive and nonlinear loads.Finally,the proposed approach addresses power flow optimization,fault mitigation,and nonlinear load management with the aim of enhancing grid stability and efficiency.
