Biswaranjan Mishra

@giet.edu

Computer Science
Research Scholar



        

https://researchid.co/biswaranjan

RESEARCH, TEACHING, or OTHER INTERESTS

Radiological and Ultrasound Technology, Computer Engineering, Computer Vision and Pattern Recognition, Information Systems

8

Scopus Publications

Scopus Publications

  • Computational analysis of gait patterns in transfemoral amputees: polycentric and weightactivated knee joints
    Sushree Sangita Nayak, Aswini Kumar Mohapatra, Srikanta Maharana, Biswaranjan Mishra, Bijay Kishor Shishir Sekhar Pattanaik, and Bijay Kumar Paikaray
    Inderscience Publishers

  • A new machine learning approach to classify MRI of brain tumour using SAE + LSTM
    Biswaranjan Mishra, Kakita Murali Gopal, Bijay Kumar Paikaray, and Srikant Patnaik
    Inderscience Publishers

  • A CNN Model with Activation to Classify MRI Images for Brain Tumor
    Biswaranjan Mishra, Kakita Murali Gopal, Srikant Patnaik, Bijay Kumar Paikaray, and Jitendra Pramanik
    AIP Publishing

  • Identification and detection of brain tumour using deep learning-based classification MRI applied using neural network and machine learning algorithm
    Biswaranjan Mishra, Kakita Murali Gopal, Srikant Patnaik, and Bijay Kumar Paikaray
    Inderscience Publishers

  • Identification of Polyp from Colonoscopy Images by Deep Belief Network based Polyp Detector Integration Model
    A. B. Dash, S. Dash, S. Padhy, R. K. Das, B. Mishra, and B. K. Paikaray
    European Alliance for Innovation n.o.
    Cancer is a disease involving unusual cell growth likely to spread to other parts of the body. According to WHO 2020 report, colorectal malignancy is the globally accepted second leading cause of cancer related deaths. Colorectal malignancy arises when malignant cells often called polyp, grow inside the tissues of the colon or rectum of the large intestine. Colonoscopy, CT scan, Histopathological analysis are some manual approaches of malignancy detection that are time consuming and lead to diagnostic errors. Supervised CNN data model requires a large number of labeled training samples to learn parameters from images. In this study we propose an expert system that can detect the colorectal malignancy and identify the exact polyp area from complex images. In this approach an unsupervised Deep Belief Network (DBN) is applied for effective feature extraction and classification of images. The classified image output of DBN is utilized by Polyp Detector. Residual network and feature extractor components of Polyp Detector helps polyp inspector in pixel wise learning. Two stage polyp network (PLPNet) is a R-CNN architecture with two stage advantage. The first stage is the extension of R-CNN to detect the polyp lesion area through a location box also called Polyp Inspector. Second Stage performs polyp segmentation. Polyp Inspector transfers the learned semantics to the polyp segmentation stage. It helps to enhance the ability to detect polyp with improved accuracy and guide the learning process. Skip schemes enrich the feature scale. Publicly available CVC-Clinical DB and CVC Colon DB datasets are used for experiment purposes to achieve a better prediction capability for clinical practices.

  • Analysis of Brain Function Effecting Form the Tumour Disease Using the Image Segmentation Technique
    Artatrana Biswaprasan Dash, Sachikanta Dash, Sasmita Padhy, Biswaranjan Mishra, and Amar Nath Singh
    IEEE
    The Human brain is the most sophisticated part of the body, which consist of several neurons and biological component. A normal brain is usually functioning at an aspect of 93% for a healthy human. The reduction of functioning is caused due to formation of solid mass known as glioma (A biological name of solid mass) in the brain. As per the convention of WHO, the formation of glioma is taking place in several stages slowly-slowly, and hence the detection of tumour is also bit difficult at the first and second stages. Its activity is also very normal what we used to face in our day to day life like stress, strain, a mild headache or even a fever which remains up to some days in the span of 2 days. Hence the detection of tumour is not usually found in these stages. When a patient enters into the third stage then it shows a symptom that may indicate that the patient may suffer from a brain tumour. In order to do the diagnosis, scanning of the brain using the well-known technique called as MRI. Here in this review, we are going to study the MRI technique in detail with its different aspects.

  • Identification of Premature Diagnosis for Detection of Brain Tumor Using Blockchain Strategy
    Artatrana Biswaprasan Dash, Biswaranjan Mishra, and Amar Nath Singh
    Springer Singapore