Publications by authors named "V Rajinikanth"

7 Publications

Automated diagnosis of amyotrophic lateral sclerosis using electromyograms and firefly algorithm based neural networks with fractional position update.

Phys Eng Sci Med 2021 Aug 16. Epub 2021 Aug 16.

Department of Electronics and Instrumentation Engineering, St. Joseph's College of Engineering, Chennai, 600119, India.

Amyotrophic Lateral Sclerosis (ALS) is a disorder of the neuromuscular system that causes the impairment of nerve cells from brain to spinal cord and to the voluntary muscles in every part of the human physiological system, which totally leads to paralysis. The examination of ALS using Electromyograms (EMG) is a challenging task which requires experts to investigate and diagnose. Hence, the development of an efficient and automated procedure is significant for the analysis of ALS signals. In this work, eighty time-frequency features were extricated from EMG signals transformed into time-frequency images. Further, fifteen highly substantial features were chosen using the firefly algorithm with fractional position update. Further, fractional firefly neural network is introduced and developed to examine the EMG signals. The performance metrics of the fractional firefly based neural network diagnostic system were analyzed with different fractional orders (α) and hidden neurons. Results demonstrated that the proposed technique is highly efficient and yields good statistical significance. Further, the accuracy of the fractional firefly neural network classifier with α  = 0.5 and 15 hidden neurons is higher (93.3%) when compared to the accuracy of the classifier with different α values and hidden neurons. The proposed fractional order-based feature selection algorithm and classifier model are highly suitable for development of systems for evaluation of ALS and normal EMG signals, since the proficient discrimination of normal and ALS EMG signals is essential for the identification of neuromuscular disorders.
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http://dx.doi.org/10.1007/s13246-021-01046-7DOI Listing
August 2021

An Optimized Method for Skin Cancer Diagnosis Using Modified Thermal Exchange Optimization Algorithm.

Comput Math Methods Med 2021 18;2021:5527698. Epub 2021 Jun 18.

Department of Electronics and Instrumentation Engineering, St. Joseph's College of Engineering, Chennai 600119, India.

Skin cancer is the most common cancer of the body. It is estimated that more than one million people worldwide develop skin cancer each year. Early detection of this cancer has a high effect on the disease treatment. In this paper, a new optimal and automatic pipeline approach has been proposed for the diagnosis of this disease from dermoscopy images. The proposed method includes a noise reduction process before processing for eliminating the noises. Then, the Otsu method as one of the widely used thresholding method is used to characterize the region of interest. Afterward, 20 different features are extracted from the image. To reduce the method complexity, a new modified version of the Thermal Exchange Optimization Algorithm is performed to the features. This improves the method precision and consistency. To validate the proposed method's efficiency, it is implemented to the American Cancer Society database, its results are compared with some state-of-the-art methods, and the final results showed the superiority of the proposed method against the others.
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http://dx.doi.org/10.1155/2021/5527698DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8235991PMC
June 2021

Medical Data Assessment with Traditional, Machine-learning and Deeplearning Techniques.

Curr Med Imaging 2020 ;16(10):1185-1186

Department of Electronics and Instrumentation Engineering, St. Joseph's College of Engineering, Chennai 600119, India.

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http://dx.doi.org/10.2174/157340561610210112143516DOI Listing
January 2020

Medical Image Examination using Traditional and Soft-computing Approaches.

Curr Med Imaging 2020 ;16(7):775

Department of Electronics and Instrumentation Engineering St. Joseph's College of Engineering Chennai 600119 Tamilnadu, India.

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http://dx.doi.org/10.2174/157340561607200909110941DOI Listing
July 2021

Social Group Optimization-Assisted Kapur's Entropy and Morphological Segmentation for Automated Detection of COVID-19 Infection from Computed Tomography Images.

Cognit Comput 2020 Aug 15:1-13. Epub 2020 Aug 15.

Department of Computing & Technology, Nottingham Trent University, Clifton Lane, Nottingham NG11 8NS UK.

The coronavirus disease (COVID-19) caused by a novel coronavirus, SARS-CoV-2, has been declared a global pandemic. Due to its infection rate and severity, it has emerged as one of the major global threats of the current generation. To support the current combat against the disease, this research aims to propose a machine learning-based pipeline to detect COVID-19 infection using lung computed tomography scan images (CTI). This implemented pipeline consists of a number of sub-procedures ranging from segmenting the COVID-19 infection to classifying the segmented regions. The initial part of the pipeline implements the segmentation of the COVID-19-affected CTI using social group optimization-based Kapur's entropy thresholding, followed by k-means clustering and morphology-based segmentation. The next part of the pipeline implements feature extraction, selection, and fusion to classify the infection. Principle component analysis-based serial fusion technique is used in fusing the features and the fused feature vector is then employed to train, test, and validate four different classifiers namely Random Forest, K-Nearest Neighbors (KNN), Support Vector Machine with Radial Basis Function, and Decision Tree. Experimental results using benchmark datasets show a high accuracy (> 91%) for the morphology-based segmentation task; for the classification task, the KNN offers the highest accuracy among the compared classifiers (> 87%). However, this should be noted that this method still awaits clinical validation, and therefore should not be used to clinically diagnose ongoing COVID-19 infection.
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http://dx.doi.org/10.1007/s12559-020-09751-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7429098PMC
August 2020
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