MIT-World Peace University (MIT-WPU) is a top educational institute located in Pune, India. It is recognized within&nbsp;UGC under Government of Maharashtra Act No. XXXV 2017.&nbsp;

It is part of the MIT group of institutions (established in 1983) which covers 10+ campuses across India and has more than 50,000+ students enrolled across various disciplines at a given point of time. The institute houses 15 schools ranging from Engineering &amp; Technology, Design, Pharmacy, Commerce, Governance among others. It offers undergraduate, postgraduate and PhD programs among different disciplines.&nbsp;

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MIT World Peace University

Coronavirus (COVID19) is a highly contagious virus which had already killed thousands of people and infected millions more throughout the world. One of the primary challenges that medical practitioners encounter in the realm of healthcare is correctly diagnosing patients conditions and infections. So far, the gold standard screening method RT-PCR test which has been designed to detect covid-19 which only has a positive rate ranging between 30 precent and 60 percent. As a result, a system that can accurately identify images and diagnose or anticipate diseases is needed. As a result, we set out to swiftly create a compact CNN architecture capable of recognizing COVID-19-infected individuals. Different CNN architectures are suggested in this paper to extract information from X-rays which further classified into Covid-19, pneumonia, or healthy. Here, we have used two datasets from publically available repositories that are Kaggle and Mendeley [1] [2]. To see how the size of datasets affects CNN performance, we train the suggested CNNs with both the original and enhanced datasets where datasets are splitted into ratios of 80:20 and 70:30 and the comparison is shown. Also suggested CNN model is compared with the five state-of-Art pre-Trained models (VGG-16, ResNet50, InceptionV3, EfficientNetB2, DenseNet121) with the same datasets and splitting ratios. we have also used Some visualization methods through which we can get an exact idea of how CNN functions and the explanation behind the network's decisions. This study suggests a model for classifying COVID-19 patients but makes no claims about medical diagnostic accuracy. © 2022 IEEE.

World University root node

School of Computer Engineering & Technology

Engineering and Technology

Proceedings - 2nd International Conference on Next Generation Intelligent Systems, ICNGIS 2022

Chest Abnormality Detection from X-Rays Using Deep Learning

Segmentation of knee joint is an instrumental task in a medical fraternity during knee osteoarthritis (KOA) progression. In general, it is a primary concern to segment, detect and extract the defects from knee magnetic resonance images (MRI). Conventionally, this process is carried out manually in clinical practice but it is time consuming and observer dependent. It is a big challenge to segment cartilage manually from MRI, as cartilage structure has inadequate image contrast and complex tissue structure. Currently, semiautomatic and automatic methods are used to overcome this limitation. In the proposed method, we present a segmentation approach for the extraction of bone and cartilage from MRI. Here, in this paper we perform preprocessing for image enhancement and noise removal of knee MR image using Gaussian blur and block matching 3D method. Larger bones in the knee joint are segmented first to perform reliable cartilage segmentation. Hence distance regularization level set evolution (DRLSE) is used for bone extraction successively followed by cartilage segmentation. Further, the performance of the proposed method is analyzed on a variety of knee MR images and experimental results demonstrated the improvement in accuracy compared to existing approaches. In conclusion, it is observed that the proposed technique improves significant performance with consistency and robustness during the segmentation process. © Springer Nature Singapore Pte Ltd. 2020.

Advances in Intelligent Systems and Computing

Segmentation of knee bone using MRI

Signature analysis helps in analyzing and understanding individual’s personality. Graphology is the scientific technique that helps us predict the writer’s personality. Different types of strokes and patterns in writer’s signature are considered for predicting their personality trait. Social skills, achievements, work habits, temperament, etc. can be predicted by using the writer’s signature. It helps us in understanding the person in a better way. As signature is directly related and develops a positive impact on your social life, personal life as well as for your career it is essential to practice correct signature for good results. The main objective here is predicting authors personality trait based on features such as Skewness, Pen Pressure, Aspect Ratio, Margin, and the difference between the first and last letter of the signature. As your signature has a direct impact on any of your assets and career, the proposed system will also provide suggestions for improvement in the signature if needed. This research paper proposes an off-line signature analysis. We have created our own dataset for the analysis purpose. We have also provided them with some questionnaire to check the accuracy of the proposed system. © 2020, Springer Nature Switzerland AG.

Personality trait with E-graphologist

Human Gait refers to motion accomplished through the movement of hand limbs. Gait analysis is a precise investigation of the human walking pattern using sensors attached to the body for recording body movements during activities like walking on a flat surface, treadmill and running. This paper addresses the analysis of human gait based on performance using various classification techniques involving Decision Tree, Random Forest and KNN algorithms. The paper highlights the comparison of these classification models based on various parameters using the RapidMiner Studio tool. The comparison is based on performance metrics and Receiver Operating Characteristic (ROC). The results show that the Random Forest algorithm performs better in classifying normal and abnormal gait. © Springer Nature Singapore Pte Ltd. 2020.

Human gait analysis based on decision tree, random forest and KNN algorithms

Articular cartilage (AC) of knee plays a vital role in the supporting mechanism of the human body. AC is a smooth, slippery surface which composed of white tissue covering the joint shell. Magnetic Resonance (MR) imaging technique is widely adopted in clinical practice to visualize and detect the defect in Knee anatomical structure. Segmentation of AC using knee MR images is an essential process to biomarker analysis during knee osteoarthritis progression. Hence it has been widely investigated by research communities. Osteoarthritis (OA) is a commonly found knee disease, in which AC is slowly and progressively lost. Early diagnosis of OA plays a significant role to look up treatment possibilities and prevent further degradation of knee AC. Segmentation methods of AC have considered various parameters of image, image quality, and tissue structure, etc. Manual segmentation of cartilage for OA diagnosis, from a large amount of MRI images produced in clinical practice, is a time-consuming and challenging process. Thus semi-automated and fully automated cartilage segmentation methods have been adopted by many researchers to provide reasonable accuracy and reliable detection of AC from knee MR images. This paper reviews knee cartilage segmentation methods from MR images. In this paper, we focus on the recent tread of knee AC segmentation techniques from MR images. First, an introduction to osteoarthritis and its causes are given. Then variety of MR imaging modalities suitable for assessment of knee OA. Finally, performance measures are explained followed by conclusion.

International Journal of Advanced Science and Technology

A Comprehensive Survey of Articular Cartilage Segmentation Methods on Knee MRI

Use of computer science in the field of medical gives the major importance to the world. Hence in the medical field we use the technology in order to help the doctor to diagnose the disease. In this paper we make use of the segmentation technique on the MRI (Magnetic Resonance Images) data to diagnose the osteoarthritis. Osteoarthritis is a knee joint disease and in this paper we are mainly focused on the cartilage degradation i.e., degrading the femur and tibia cartilage. Image Segmentation provides the analysis of the image and extracts the data in order to perform the operation on it. This paper lists out various image segmentation techniques, to perform the segmentation of the knee cartilage from magnetic resonance images (MRI). The segmentation technique by the rough fuzzy c means classifier which provides the degrade in cartilage region and the SVM (support vector Machine) technique is used to classify the degradation cartilage. © 2018 IEEE.