Cervical Cancer(CC)is a rapidly growing disease among women throughout the world,especially in developed and developing countries.For this many women have died.Fortunately,it is curable if it can be diagnosed and dete...Cervical Cancer(CC)is a rapidly growing disease among women throughout the world,especially in developed and developing countries.For this many women have died.Fortunately,it is curable if it can be diagnosed and detected at an early stage and taken proper treatment.But the high cost,awareness,highly equipped diagnosis environment,and availability of screening tests is a major barrier to participating in screening or clinical test diagnoses to detect CC at an early stage.To solve this issue,the study focuses on building a deep learning-based automated system to diagnose CC in the early stage using cervix cell images.The system is designed using the YOLOv5(You Only Look Once Version 5)model,which is a deep learning method.To build the model,cervical cancer pap-smear test image datasets were collected from an open-source repository and these were labeled and preprocessed.Then the YOLOv5 models were applied to the labeled dataset to train the model.Four versions of the YOLOv5 model were applied in this study to find the best fit model for building the automated system to diagnose CC at an early stage.All of the model’s variations performed admirably.The model can effectively detect cervical cancerous cell,according to the findings of the experiments.In the medical field,our study will be quite useful.It can be a good option for radiologists and help them make the best selections possible.展开更多
In this paper, a hexagonal shape photonic crystal fiber （H-PCF） has been proposed as a gas sensor of which both micro-structured core and cladding are organized by circular air cavities. The reported H-PCF has a sin...In this paper, a hexagonal shape photonic crystal fiber （H-PCF） has been proposed as a gas sensor of which both micro-structured core and cladding are organized by circular air cavities. The reported H-PCF has a single layer circular core which is surrounded by a five-layer hexagonal cladding. The overall pretending process of the H-PCF is completed by using a full vectorial finite element method （FEM） with perfectly matched layer （PML） boundary condition. All geometrical parameters like diameters and pitches of both core and cladding regions have fluctuated with an optimized structure. After completing the numerical analysis, it is clearly visualized that the proposed H-PCF exhibits high sensitivity with low confinement loss. The investigated results reveal the relative sensitivity of 56.65% and confinement loss of 2.31×10^-5 dB/m at the 1.33%tm wavelength. Moreover, effective area, nonlinearity, and V-parameter of the suggested PCF are also briefly described.展开更多
基金The project funding number is 22UQU4170008DSR07the Natural Sciences and Engineering Research Council of Canada(NSERC).
文摘Cervical Cancer(CC)is a rapidly growing disease among women throughout the world,especially in developed and developing countries.For this many women have died.Fortunately,it is curable if it can be diagnosed and detected at an early stage and taken proper treatment.But the high cost,awareness,highly equipped diagnosis environment,and availability of screening tests is a major barrier to participating in screening or clinical test diagnoses to detect CC at an early stage.To solve this issue,the study focuses on building a deep learning-based automated system to diagnose CC in the early stage using cervix cell images.The system is designed using the YOLOv5(You Only Look Once Version 5)model,which is a deep learning method.To build the model,cervical cancer pap-smear test image datasets were collected from an open-source repository and these were labeled and preprocessed.Then the YOLOv5 models were applied to the labeled dataset to train the model.Four versions of the YOLOv5 model were applied in this study to find the best fit model for building the automated system to diagnose CC at an early stage.All of the model’s variations performed admirably.The model can effectively detect cervical cancerous cell,according to the findings of the experiments.In the medical field,our study will be quite useful.It can be a good option for radiologists and help them make the best selections possible.
文摘In this paper, a hexagonal shape photonic crystal fiber （H-PCF） has been proposed as a gas sensor of which both micro-structured core and cladding are organized by circular air cavities. The reported H-PCF has a single layer circular core which is surrounded by a five-layer hexagonal cladding. The overall pretending process of the H-PCF is completed by using a full vectorial finite element method （FEM） with perfectly matched layer （PML） boundary condition. All geometrical parameters like diameters and pitches of both core and cladding regions have fluctuated with an optimized structure. After completing the numerical analysis, it is clearly visualized that the proposed H-PCF exhibits high sensitivity with low confinement loss. The investigated results reveal the relative sensitivity of 56.65% and confinement loss of 2.31×10^-5 dB/m at the 1.33%tm wavelength. Moreover, effective area, nonlinearity, and V-parameter of the suggested PCF are also briefly described.
