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An Automated Deep Learning Based Muscular Dystrophy Detection and Classification Model

Computers, Materials & Continua 2022年第4期71卷 305-320页

作者： T.Gopalakrishnan periakaruppan sudhakaran K.C.Ramya K.Sathesh Kumar Fahd N.Al-Wesabi Manal Abdullah Alohali Anwer Mustafa Hilal School of Computer Science and Engineering Vellore Institute of TechnologyVellore632014India Department of Computer Science and Engineering SRM TRP Engineering CollegeTiruchirappalli621105India Department of Electrical and Electronics Engineering Sri Krishna College of Engineering and TechnologyCoimbatore641008India School of Computing Kalasalingam Academy of Research and EducationKrishnankoil626128India Department of Computer Science King Khalid UniversityMuhayel AseerKSA Faculty of Computer and IT Sana’a UniversityYemen Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia

Muscular Dystrophy (MD) is a group of inherited muscular diseases that are commonly diagnosed with the help of techniques such asmuscle biopsy, clinical presentation, and Muscle Magnetic Resonance Imaging(MRI). Among these techniques, Muscle MRI recommends the diagnosis ofmuscular dystrophy through identification of the patterns that exist in musclefatty replacement. But the patterns overlap among various diseases whereasthere is a lack of knowledge prevalent with regards to disease-specific ***, artificial intelligence techniques can be used in the diagnosis ofmuscular dystrophies, which enables us to analyze, learn, and predict forthe future. In this scenario, the current research article presents an automated muscular dystrophy detection and classification model using SynergicDeep Learning (SDL) method with extreme Gradient Boosting (XGBoost),called SDL-XGBoost. SDL-XGBoost model has been proposed to act as anautomated deep learning (DL) model that examines the muscle MRI dataand diagnose muscular dystrophies. SDL-XGBoost model employs Kapur’sentropy based Region of Interest (RoI) for detection purposes. Besides, SDLbased feature extraction process is applied to derive a useful set of featurevectors. Finally, XGBoost model is employed as a classification approach todetermine proper class labels for muscle MRI data. The researcher conductedextensive set of simulations to showcase the superior performance of SDLXGBoost model. The obtained experimental values highlighted the supremacyof SDL-XGBoost model over other methods in terms of high accuracy being96.18% and 94.25% classification performance upon DMD and BMD respectively. Therefore, SDL-XGBoost model can help physicians in the diagnosis of muscular dystrophies by identifying the patterns of muscle fatty replacementin muscle MRI.

关键词： Muscle magnetic resonance imaging XGBoost synergic deep learning roI detection kapur’s entropy muscular dystrophies

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Benevolent behavior of Kleinia grandiflora leaf extract as a green corrosion inhibitor for mild steel in sulfuric acid solution

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International Journal of Minerals,Metallurgy and Materials 2014年第11期21卷 1083-1095页

作者： Muthukrishnan Pitchaipillai Karthik Raj Jeyaprabha Balasubramanian Prakash periakaruppan Department of Chemistry Faculty of Engineering Karpagam UniversityCoimbatore-641021 Tamil Nadu India Department of Chemistry Thiagarajar CollegeMadurai-625009 Tamil Nadu India Department of Civil Engineering Fatima Michael College of Engineering & TechnologyMadurai-625020 Tamil Nadu India

The ethanolic extract of Kleinia grandiflora leaves was characterized and tested for its potential anticorrosion properties on mild steel in 1 M H2SO4 medium using mass-loss analysis, potentiodynamic polarization measurements, electrochemical impedance spectroscopy, Fourier-transform infrared spectroscopy, scanning electron microscopy, UV–visible spectroscopy, and X-ray diffraction analysis. The effect of temperature on the corrosion behavior of mild steel was studied in the range of 308 to 328 K. The inhibition efficiency was observed to increase with increasing concentration of the extract. Polarization curves revealed that the Kleinia grandiflora leaf extract is a mixed inhibitor. Impedance diagrams revealed that an increase of Kleinia grandiflora leaf extract concentration increased the charge transfer resistance and decreased the double-layer capacitance. The adsorption process obeys Langmuir＇s model, with a standard free energy of adsorption（ΔGads） of-18.62 k J/mol. The obtained results indicate that the Kleinia grandiflora leaf extract can serve as an effective inhibitor for the corrosion of mild steel in a sulfuric acid medium.

关键词： steel corrosion mild steel sulfuric acid corrosion inhibitors

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