Papers by imayanmosha wahlang
Sensors, Feb 24, 2022
This article is an open access article distributed under the terms and conditions of the Creative... more This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
Lecture notes in electrical engineering, 2020
Lecture notes in networks and systems, 2021
Lecture notes in networks and systems, 2018
With the help of computer science in today’s world many advances have been made in the field of f... more With the help of computer science in today’s world many advances have been made in the field of facial expression recognition. It is considered as an important measure of social interactions and there are many applications that can be helpful to human in many ways as facial expressions not only show the emotional status of an individual but also other mental activities as well. To develop such a system is a challenging task as it involves learning and understanding of expressions of human which varies from time to time. In this paper, a method on facial expression analysis has been proposed where face detection was done using RGB color model and feature classification using Gabor filter.
Lecture notes in networks and systems, 2018
Human face recognition is very interesting as well as very challenging area of research. In our s... more Human face recognition is very interesting as well as very challenging area of research. In our study of different existing face recognition schemes, we have seen that it has very important role in many applications. For this paper, we have studied and worked on the various steps of face recognition and analyzed a method to work on them. Where the face is detected from an input image and Gabor filter is applied. Then, two approaches are used (1) Delaunay triangulation and (2) Euclidean distance which are apply to the extracted feature points (which is referred as node set graph) and they are stored in the database. These store node set is used for checking the similarity with the input image. We have also compared both of the proposed approach in the system and we found that node set with Euclidean distance gives a better recognition rate.
Sensors, 2022
Usage of effective classification techniques on Magnetic Resonance Imaging (MRI) helps in the pro... more Usage of effective classification techniques on Magnetic Resonance Imaging (MRI) helps in the proper diagnosis of brain tumors. Previous studies have focused on the classification of normal (nontumorous) or abnormal (tumorous) brain MRIs using methods such as Support Vector Machine (SVM) and AlexNet. In this paper, deep learning architectures are used to classify brain MRI images into normal or abnormal. Gender and age are added as higher attributes for more accurate and meaningful classification. A deep learning Convolutional Neural Network (CNN)-based technique and a Deep Neural Network (DNN) are also proposed for effective classification. Other deep learning architectures such as LeNet, AlexNet, ResNet, and traditional approaches such as SVM are also implemented to analyze and compare the results. Age and gender biases are found to be more useful and play a key role in classification, and they can be considered essential factors in brain tumor analysis. It is also worth noting th...
Information and Communication Technology for Competitive Strategies, 2018
Segmentation of brain tumor from medical images is an interesting topic which is investigated by ... more Segmentation of brain tumor from medical images is an interesting topic which is investigated by many researchers. It is important to locate tumor at an early stage so that it can be easily healed and can be used for further diagnosis. There are different imaging techniques which are used in segmentation of brain tumor. Among them, Magnetic Resonance Imaging (MRI) is most widely used radiological tool as it is radiation free in nature. For detecting the size, shape, and location of the tumor many segmentation algorithms were used. In this paper, an exhaustive study of brain tumor from MRI images has made by using different techniques. A comparative study and performance evaluation of different techniques based on certain performance metrics are also discussed in this paper.
Human face recognition is very interesting as well as very challenging area of research. In our s... more Human face recognition is very interesting as well as very challenging area of research. In our study of different existing face recognition schemes, we have seen that it has very important role in many applications. For this paper, we have studied and worked on the various steps of face recognition and analyzed a method to work on them. Where the face is detected from an input image and Gabor filter is applied. Then, two approaches are used (1) Delaunay triangulation and (2) Euclidean distance which are apply to the extracted feature points (which is referred as node set graph) and they are stored in the database. These store node set is used for checking the similarity with the input image. We have also compared both of the proposed approach in the system and we found that node set with Euclidean distance gives a better recognition rate.
With the help of computer science in today’s world many advances have been made in the field of f... more With the help of computer science in today’s world many advances have been made in the field of facial expression recognition. It is considered as an important measure of social interactions and there are many applications that can be helpful to human in many ways as facial expressions not only show the emotional status of an individual but also other mental activities as well. To develop such a system is a challenging task as it involves learning and understanding of expressions of human which varies from time to time. In this paper, a method on facial expression analysis has been proposed where face detection was done using RGB color model and feature classification using Gabor filter.
Electronics, 2021
This article experiments with deep learning methodologies in echocardiogram (echo), a promising a... more This article experiments with deep learning methodologies in echocardiogram (echo), a promising and vigorously researched technique in the preponderance field. This paper involves two different kinds of classification in the echo. Firstly, classification into normal (absence of abnormalities) or abnormal (presence of abnormalities) has been done, using 2D echo images, 3D Doppler images, and videographic images. Secondly, based on different types of regurgitation, namely, Mitral Regurgitation (MR), Aortic Regurgitation (AR), Tricuspid Regurgitation (TR), and a combination of the three types of regurgitation are classified using videographic echo images. Two deep-learning methodologies are used for these purposes, a Recurrent Neural Network (RNN) based methodology (Long Short Term Memory (LSTM)) and an Autoencoder based methodology (Variational AutoEncoder (VAE)). The use of videographic images distinguished this work from the existing work using SVM (Support Vector Machine) and also ...
Advances in Intelligent Systems and Computing, 2020
MR images are popularly used as a tool in diagnosis of brain tumors. It is widely used because of... more MR images are popularly used as a tool in diagnosis of brain tumors. It is widely used because of its varieties of angles and clarity of anatomy. Brain tumor is dangerous if it is malignant or secondary tumor. These kinds of tumor can easily spread from one location to another. Expertise and human intervention are needed to detect any kind of abnormalities like tumor, etc., from MR image. So, if we can use an automated brain tumor detection methodology to predict the presence of tumor in brain without human intervention, it will provide an edge in the process of treatment to this disease. Classification plays a vital role in detection of brain tumor. Taking into account the importance of detection of brain tumor, this paper analyzes four architectures of convolutional neural networks (CNN) for classification of brain MR images into tumorous or nontumorous in unsupervised manner. The architectures which are discussed in this paper are ConvNet, Lenet, ResNet, and Densenet.
International Journal of Intelligent Systems Technologies and Applications, 2020
Several brain diseases are becoming a threat to the livelihood of people. One such problem is the... more Several brain diseases are becoming a threat to the livelihood of people. One such problem is the presence of a brain tumour. A brain tumour can be benign or malignant. It is dangerous if it is a malignant or secondary tumour (metastasis). Therefore, there is a need to detect the presence of tumours at the earliest stage as possible. Using an automated method for brain tumour detection can be a solution to medical expertise as a biopsy can be excluded if early detection could be possible. Classification helps in the prediction of the type of image and type of tumour. In this paper, three stages are involved. In the first stage, the classification of brain MR images into normal (tumour) or abnormal (non-tumour) images using ConvNet, LeNet, ResNet, and DenseNet has been analysed. In the second stage, architectures like LeNet and AlexNet are used in the prediction of the type of tumour namely metastasis, glioma, and meningioma. And lastly, using U-Net and AlexNet, classification into high grade glioma and low grade glioma was done.
Research Journal of Pharmacy and Technology, 2018
Applied Sciences
Echocardiography (echo) is a commonly utilized tool in the diagnosis of various forms of valvular... more Echocardiography (echo) is a commonly utilized tool in the diagnosis of various forms of valvular heart disease for its ability to detect types of cardiac regurgitation. Regurgitation represents irregularities in cardiac function and the early detection of regurgitation is necessary to avoid invasive cardiovascular surgery. In this paper, we focussed on the classification of regurgitations from videographic echo images. Three different types of regurgitation are considered in this work, namely, aortic regurgitation (AR), mitral regurgitation (MR), and tricuspid regurgitation (TR). From the echo images, texture features are extracted, and classification is performed using Random Forest (RF) classifier. Extraction of keyframe is performed from the video file using two approaches: a reference frame keyframe extraction technique and a redundant frame removal technique. To check the robustness of the model, we have considered both segmented and nonsegmented frames. Segmentation is carrie...
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Papers by imayanmosha wahlang