S Visalaxi

Hindustan Institute of Technology and Science

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T. Sudalaimuthu

Hindustan Institute of Technology and Science

T. Sudalai Muthu

Government Vellore Medical College

Dinah Punnoose

Hindustan Institute of Technology and Science

Sudalaimuthu Thalavaipillai

Hindustan Institute of Technology and Science

K. Hemapriya

Sowmya. V.J

Hindustan Institute of Technology and Science

Varadharaj Krithika

Hindustan Institute of Technology and Science

Surya Rajeswar

Hindustan Institute of Technology and Science

Sachin Kamal

Hindustan Institute of Technology and Science

Pon Ramlingam

Hindustan Institute of Technology and Science

Cooperative Institutions

Hindustan Institute of Technology and Science

Yes Technologies (United States)

Vellore Institute of Technology University

Pusan National University

Government of the Republic of Korea

Google (United States)

Adithya Institute of Technology

Government Vellore Medical College

Waikato Hospital

Royal Victoria Hospital

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Analysis and Forecasting Covid-19 Spread in India Using Logistic Regression and Prophet Time Series

2021 International Conference on Computational Performance Evaluation (ComPE) (2021)

Ashok Verghese T. Sudalaimuthu S Visalaxi

Covid19 pandemic is infecting a large community across the globe. Nearly 29 million got affected due to covid in India. The cases are increasing day by day. There are confirmed cases along with recovery and fatality rate. Prediction of the growth /fatality rate is a challengeable one. This paper implements an Artificial Intelligence (AI) strategy for analyzing and predicting the covid cases across the nation on daily basis at various rates. It includes (a) Analysis of growth rate, (b) Prediction of Confirmed rate, (c) Prediction of Deceased Rate, (d) Analysis of Recovery rate, etc. Logistic regression (LR) is a classification problem that performs well on medical data. The proposed work implements logistic regression along with prophet methods for analyzing the time-based covid cases across India. This proposed work analyzes the active cases and perform them effectively with an accuracy of 0.96.

Case fatality rate

Pandemic

10.1109/compe53109.2021.9752218

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Citations (2)

Endometrium Phase prediction using K-means Clustering through the link of Diagnosis and procedure

S Visalaxi T. Sudalaimuthu

The endometriosis was a malignancy reproductive problem. The tissue like structure look at the exterior side of the uterus. The endometriosis was found among 10% of global women population. Endometriosis affect other region includes pelvis, gall bladder, intestines etc. The prediction of endometriosis was done through various ways. Laparoscopic procedure is considered as standard method for visualizing the existence of endometriosis. The proposed study uses K-means clustering algorithm for linking the association of diagnosis of endometriosis with the procedure applied to treat the endometriosis. This algorithm form the clusters to differentiate the phases of endometrium at various level. The K-mean algorithm performance was analyzed using silhouette score metrics. The score was compared with principal component analysis .The K-means algorithm yields the silhouette score of 0.198.

10.1109/spin52536.2021.9566041

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Automated prediction of endometriosis using deep learning

International journal of nonlinear analysis and applications (2021)

S Visalaxi T. Sudalai Muthu

Endometriosis is the anomalous progress of cells at the outer part of the uterus. Generally, this endometrial tissue stripes the uterine cavity. The existence of endometriosis is identified through procedures known as Transvaginal Ultra Sound Scan (TVUS), Magnetic Resonance Imaging (MRI), Laparoscopic procedures, and Histopathological slides. Minimal Invasive Surgery (MIS) Laparo-scopic images are recorded in a small camera. To assist the surgeon in identifying their presence of endometriosis, image quality (characteristics) was enhanced for more visual clarity. Deep learning has the ability in recognising the images for classification. The Convolutional Neural Networks (CNNs) perform classification of images on large datasets. The proposed system evaluates the performance by a novel approach that implements the transfer learning model on a well-known architecture called ResNet50. The proposed system train the model on ResNet50 architecture and yielded a training accuracy of 91%, validation accuracy of 90%, precision of 83%, and recall of 82%, which can be applied for larger datasets with better performance. The presented system yields higher Area Under Curve (AUC) of about 0.78. The proposed method yields better performance using ResNet50 compared to other transfer learning techniques.

Transfer of learning

10.22075/ijnaa.2021.5383

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Automatic selection of routers using YANG tool

AIP conference proceedings (2022)

T. Sudalai Muthu Sachin Kamal S Visalaxi

Network protocol plays a major role in data transfer between routers and switches. There exist several approaches to manage routers for configuration and operation of data using Command Line Interface (CLI). The SNMP is used to mange the network devices. The problem faced by SNMP is that the configuration is done manually. In order to overcome the manual process the proposed system known as YANG/NETCONF came into existence. The YANG is modelling language along with NETCONF that configures the network devices automatically. The proposed work is using YANG to design automatic identification system. The automatic identification system is used to select the router in the network. The designed system is experimented using the YANG tool and the accuracy and the time taken to identify the appropriate router are recorded. The current proposed system is designed as new app that allowed all users to choose their desirable testbed/routers under the series NCS 500 and NCS 5500. The automatic identification system yielded 98.23% accuracy and 150ms as time taken to locate the appropriate routers take into identify the router.

Testbed

Identification

Interface (matter)

10.1063/5.0071070

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Endometriosis Laparoscopic Image Reconstruction Using PCA and IPCA

2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA) (2021)

S Visalaxi T. Sudalaimuthu Varadharaj Krithika

Principle component analysis is a "state-of-art" technique for handling medical-based image data. The Laparoscopic images of Endometriosis are in raw format and large volumes and hence the images cannot be used directly for further processing. The Principal Component Analysis (PCA) has been used to handle such a large volume of images without affecting pixel intensity. A algorithm has been proposed using PCA & IPCA to identify the exact components needed to compress the images. The size of the image has been reduced by implementing PCA Eigenvectors and Eigenvalues. The best component value identified through IPCA is 300 where the variance is 95 %.

10.1109/icaeca52838.2021.9675620

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Automated Segmentation of Endometriosis using Transfer Learning

Zenodo (CERN European Organization for Nuclear Research) (2022)

S Visalaxi T. Sudalaimuthu

Transfer of learning

10.5281/zenodo.6324521

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Lesion Extraction of Endometriotic images using Open Computer Vision

S Visalaxi Dinah Punnoose T. Sudalai Muthu

The Endometriosis is caused by the endometriotic tissue that streaks the uterus from outside. A total of 6% of women under child bearing age is affected by the endometriosis disorder. The presence of the tissue can be identified though MRI, Transvaginal Ultra Sound Scan (TVUS), Laparoscopic images and pathological slides. Image based applications are processed using Image Pre-processing, Image Segmentation, Image Enhancement and Feature Extraction techniques. The Open Computer Vision (Open CV) is used to extract the features from Laparoscopic Endometriosis Images (LPI). This paper proposed a method to identify the endometriotic tissue by using the LPI images. The proposed method uses several image processing techniques of OpenCV includes Adaptive Threshold, Contour Mask to extract the lesion area from the Laparoscopic images. The designed algorithm has been experimented and the results yielded 53.5 % mean value intensity in identifying the lesion area.

Feature (linguistics)

10.1109/icais50930.2021.9395822

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Citations (13)

Symptoms based endometriosis prediction using machine learning

Bulletin of Electrical Engineering and Informatics (2021)

S Visalaxi Sudalaimuthu Thalavaipillai

Endometriosis a painful disorder that stripes the uterus both inside and outside. Endometriosis can be diagnosed by the medical practitioners with the help of traditional scanning procedures. Laparoscopic surgery is the authentic method for identifying the advanced stages of endometriosis. The statistical approach is a state-of-art method for identifying the various stages of endometriosis using laparoscopic images. The paper focuses on a well-known statistical method known as chi-square and correlation coefficients are implemented for identifying the symptoms that are correlated with various stages of endometriosis. Chi-square analysis performs the association between symptoms and stages of endometriosis. With these analysis, an algorithm was proposed known as endometriosis prediction factor algorithm (EPF). The EPF algorithm predicts the presence of endometriosis if the derived value is greater than 1. From the chi-square analysis, it is identified that mild endometriosis is influenced 34% by menstrual flow, minimal endometriosis is influenced 40% by dysmenorrhea, where moderate endometriosis is influenced 31% by tenderness and deep infiltrating endometriosis is influenced 22% by adnexal mass.

10.11591/eei.v10i6.3254

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Citations (15)

Automated segmentation of endometriosis using transfer learning technique

F1000Research (2022)

S Visalaxi T. Sudalaimuthu

Background: This paper focuses on segmenting the exact location of endometriosis using the state-of-art technique known as U-Net. Endometriosis is a progressive disorder that has a significant impact on women. The lesion-like appearance that grows inside the uterus and sheds for every periodical cycle is known as endometriosis. If the lesion exists and is transferred to other locations in the women’s reproductive system, it may lead to a serious problem. Besides radiologists deep learning techniques exist for recognizing the presence and aggravation of endometriosis. Methods: The proposed method known as structural similarity analysis of endometriosis (SSAE) identifies the similarity between pathologically identified and annotated images obtained from standardized dataset known as GLENDA v1.5 by implementing two systematic approaches. The first approach is based on semantic segmentation and the second approach uses statistical analysis. Semantic segmentation is a cutting-edge technology for identifying exact locations by performing pixel-level classification. In semantic segmentation, U-Net is a transfer-learning architecture that works effectively for biomedical image classification. The SSAE implements the U-Net architecture for segmenting endometriosis based on the region of occurrence. The second approach proves the similarity between pathologically identified images and the corresponding annotated images using a statistical evaluation. Statistical analysis was performed using calculation of both the mean and standard deviation of all four regions by implementing systematic sampling procedure. Results: The SSAE obtains the intersection over union value of 0.72 and the F1 score of 0.74 for the trained dataset. The means of both the laparoscopic and annotated images for all regions were similar. Consequently, the SSAE facilitated the presence of abnormalities in a specific region. Conclusions: The proposed SSAE approach identifies the affected region using U-Net architecture and systematic sampling procedure.

10.12688/f1000research.110283.1

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Citations (1)

Automated segmentation of endometriosis using transfer learning technique

F1000Research (2022)

S Visalaxi T. Sudalaimuthu

10.12688/f1000research.110283.2

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Citations (3)