Soft computing based color image demosaicing for medical Image processing

Every year, cervical cancer (CC) is the leading cause of death in women around the world. If detected early enough, this cancer can be treated, and patients will receive adequate care. This study introduces a novel ultrasound-based method for detecting CC. The Oriented Local Histogram Technique (OLHT) is used to improve the image corners in the cervical image (CI), and the Dual-Tree Complex Wavelet Transform (DT-CWT) is used to build a multi-resolution image (CI). Wavelet, and Local Binary Pattern are among the elements retrieved from this improved multi-resolution CI (LBP). The retrieved appearance is trained and tested using a feed-forward propagation neural network, and the ANFIS classifier is utilized to classify them. The purpose of this classifier is to distinguish between normal and pathological cervical pictures. Sensitivity is 97.52 percent, specificity is 99.46 percent, accuracy is 98.39 percent, precision is 97.48 percent, PPV is 97.38 percent, NPV is 92.27 percent, LRP is 141.81 percent, 0.0946 percent LRN, FPR is 96.82 percent, and NPR is 91.46 percent for the CC detection categorization. The proposed methodology outperforms standard CC identification and classification methodologies.

Section: Methodsmentioning

confidence: 99%

Segmentation of Cervical Cancer by OLHT Based DT-CWT Techniques

Rani¹,

Rose²

2022

“…The design is focused on the effect of kinematic viscosity and water content of the natural esters on its breakdown voltage. The input variables of the fuzzy logic controller are the kinematic viscosity and water content, and the output variable, breakdown voltage measured according to the standards of IEC [35] and ASTM [36][37][38][39][40][41]. The fuzzy logic controllers for NE1, NE2, and NE3 are shown in Fig.…”

Section: Prediction Of Dielectric Properties Using Fuzzy Logicmentioning

confidence: 99%

Breakdown Voltage Prediction by Utilizing the Behavior of Natural Ester for Transformer Applications

Pakianathan¹,

Maheswari²

2023

This research investigates the dielectric performance of Natural Ester (NE) using the Partial Differential Equation (PDE) tool and analyzes dielectric performance using fuzzy logic. NE nowadays is found to replace Mineral Oil (MO) due to its extensive dielectric properties. Here, the heat-tolerant Natural Esters Olive oil (NE1), Sunflower oil (NE2), and Ricebran oil (NE3) are subjected to High Voltage AC (HVAC) under different electrodes configurations. The breakdown voltage and leakage current of NE1, NE2, and NE3 under Point-Point (P-P), Sphere-Sphere (S-S), Plane-Plane (PL-PL), and Rod-Rod (R-R) are measured, and survival probability is presented. The electric field distribution is analyzed using the Partial Differential Equation (PDE) tool. NE shows better HVAC with stand capacity under all the electrodes configuration, especially in the S-S shape geometry. The exponential function is developed for the oils under different electrode geometry; NE shows a higher survival probability. Likewise, the most influential dielectric properties such as breakdown voltage, kinematic viscosity, and water content are used to develop a Mamdani-based control system model that combines two variables to produce the surface model. The surface model indicates that the NE subjected for investigation is less susceptible to moisture effect and higher kinematic viscosity. Based on the surface models of PDE and fuzzy, it is concluded that NE possesses a high survival rate since its breakdown voltage does not react to changes in water content. Hence the application of NE in the transformer application is highly safe and possesses extended life.

“…The proposed approach appears to be more viable and stable. The approach can also be used to differentiate distinct highlights and characteristics in chemical imbalance children's facial photographs, such as activity units, enthusiasm, and valence [20][21][22].…”

Section: Related Workmentioning

confidence: 99%

Facial Action Coding and Hybrid Deep Learning Architectures for Autism Detection

Saranya¹,

Anandan²

2022

Hereditary Autism Spectrum Disorder (ASD) is a neuron disorder that affects a person's ability for communication, interaction, and also behaviors. Diagnostics of autism are available throughout all stages of life, from infancy through adolescence and adulthood. Facial Emotions detection is considered to be the most parameter for the detection of Autismdisorders among the different categories of people. Propelled with a machine and deep learning algorithms, detection of autism disorder using facial emotions has reached a new dimension and has even been considered as the precautionary warning system for caregivers. Since Facial emotions are limited to only seven expressions, detection of ASD using facial emotions needs improvisation in terms of accurate detection and diagnosis. In this paper, we empirically relate the facial emotions to the ASD using the Facial Action Coding Systems (FACS) in which the different features are extracted by the FACS systems. For feature extraction, DEEPFACENET uses the FACS integrated Convolutional Neural Network (FACS-CNN) and hybrid Deep Learning of LSTM (Long Short-Term Memory) for the classification and detection of autism spectrum disorders (ASD). The experimentation is carried out using AFFECTNET databases and validated using Kaggle Autistic facial datasets (KAFD-2020). The Multi-Layer Perceptron (48.67%), Convolutional neural networks (67.75%), and Long ShortTerm Memory (71.56), the suggested model showed a considerable increase in recognition rate (92%), from this proposed model prove its superiority in detecting autistic facial emotions among children effectively.