Computational Approach for Automated Segmentation and Classification of Region of Interest in Lateral Breast Thermograms

Breast cancer is one of the major health issues with high mortality rates and a substantial impact on patients and healthcare systems worldwide.Various Computer-Aided Diagnosis(CAD)tools,based on breast thermograms,have been developed for early detection of this disease.However,accurately segmenting the Region of Interest(ROI)fromthermograms remains challenging.This paper presents an approach that leverages image acquisition protocol parameters to identify the lateral breast region and estimate its bottomboundary using a second-degree polynomial.The proposed method demonstrated high efficacy,achieving an impressive Jaccard coefficient of 86%and a Dice index of 92%when evaluated against manually created ground truths.Textural features were extracted from each view’s ROI,with significant features selected via Mutual Information for training Multi-Layer Perceptron(MLP)and K-Nearest Neighbors(KNN)classifiers.Our findings revealed that the MLP classifier outperformed the KNN,achieving an accuracy of 86%,a specificity of 100%,and an Area Under the Curve(AUC)of 0.85.The consistency of the method across both sides of the breast suggests its viability as an auto-segmentation tool.Furthermore,the classification results suggests that lateral views of breast thermograms harbor valuable features that can significantly aid in the early detection of breast cancer.

Gaussian PI Controller Network Classifier for Grid-Connected Renewable Energy System

Multi-port converters are considered as exceeding earlier period decade owing to function in a combination of different energy sources in a single processing unit.Renewable energy sources are playing a significant role in the modern energy system with rapid development.In renewable sources like fuel combustion and solar energy,the generated voltages change due to their environmental changes.To develop energy resources,electric power generation involved huge awareness.The power and output voltages are plays important role in our work but it not considered in the existing system.For considering the power and voltage,Gaussian PI Controller-Maxpooling Deep Convolutional Neural Network Classifier(GPIC-MDCNNC)Model is introduced for the grid-connected renewable energy system.The input information is collected from two input sources.After that,input layer transfer information to hidden layer 1 fuzzy PI is employed for controlling voltage in GPIC-MDCNNC Model.Hidden layer 1 is transferred to hidden layer 2.Gaussian activation is employed for determining the output voltage with help of the controller.At last,the output layer offers the last value in GPIC-MDCNNC Model.The designed method was confirmed using one and multiple sources by stable and unpredictable input voltages.GPIC-MDCNNC Model increases the performance of grid-connected renewable energy systems by enhanced voltage value compared with state-of-the-art works.The control technique using GPIC-MDCNNC Model increases the dynamics of hybrid energy systems connected to the grid.

An Efficient Image Analysis Framework for the Classification of Glioma Brain Images Using CNN Approach

The identification of brain tumors is multifarious work for the separation of the similar intensity pixels from their surrounding neighbours.The detection of tumors is performed with the help of automatic computing technique as presented in the proposed work.The non-active cells in brain region are known to be benign and they will never cause the death of the patient.These non-active cells follow a uniform pattern in brain and have lower density than the surrounding pixels.The Magnetic Resonance(MR)image contrast is improved by the cost map construction technique.The deep learning algorithm for differentiating the normal brain MRI images from glioma cases is implemented in the proposed method.This technique permits to extract the linear features from the brain MR image and glioma tumors are detected based on these extracted features.Using k-mean clustering algorithm the tumor regions in glioma are classified.The proposed algorithm provides high sensitivity,specificity and tumor segmentation accuracy.

Performance evaluation of natural esters and dielectric correlation assessment using artificial neural network(ANN)

The performance of correlation between the dielectric parameters of Baobab Oil(BAO)and Mongongo Oil(MGO)is evaluated using Artificial Neural Network(ANN).The BAO and MGO naturally own high Unsaturated Fatty Acids(UFAs)and are highly biodegradable.The temperature studies and dielectric studies are carried out and found that the Natural Esters(NEs)show a reliable performance over mineral oil-based Transformer Oil(TO).Further the endurance test,Partial Discharge Inception Voltage(PDIV)repetition rate and drop after 30 days,dielectric measurements are done as per the standards of IEC(International Electrotechnical Commission)and ASTM(American Society for Testing and Materials).The NEs show stable performance under PDIV and show minimum repetition rate when compared to the TO.The C10H22 or Kerosene(KER)and NEs mixture prove that the NE-based transformer fluids show lesser tendency to hydro peroxidation.The C10H22 acts as a thinning agent and reduces the ageing rate of the NEs,and this leads to slower rate of water saturation.This in turn increases the thermal conductivity of the oil and nearly a 30-days thermal ageing of the oil samples at 90°C shows better strength of liquid insulation.The performance of association between the dielectric properties like breakdown voltage and water content,dissipation factor and thermal conduc-tivity prove that the NEs show consistent performance and is a better substitute for the mineral oil-based TO.