A new machine learning approach to classify MRI of brain tumour using SAE + LSTM Biswaranjan Mishra, Kakita Murali Gopal, Bijay Kumar Paikaray, Srikant Patnaik International Journal of Bioinformatics Research and Applications, 2024 A brain tumour is a serious condition that can seriously harm brain cells and eventually progress to cancer, which is life-threatening. The patient's chances of survival can be improved when the tumour stages are detected early. The proposed tumour diagnosis uses a fused feature set to increase the classifier's accuracy. To begin with, the features from the MRI images are extracted using the grey level co-occurrence matrix (GLCM) and histogram of oriented gradients (HOG). After dimensionality reduction, features are chosen with stacked autoencoder (SAE). Second, the high-level features from the MRI images are extracted using the channel-wise attention block. The long short-term memory (LSTM) is trained to produce the results of the classification using the fused features from SAE and the attention block. The proposed approach is evaluated with the BRATS dataset for the years 2018-1020. The accuracy attained over various datasets is 97%, 95.56% and 95.23%.
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, Bijay Kumar Paikaray International Journal of Computational Biology and Drug Design, 2024 In this article, we fitted polycentric or weight-activated knee joints to ten young, active amputees with transfemoral amputations. We compared the gait patterns of the amputees with those of 10 individuals using a polycentric or weight-activated knee. We compared the performance of participants with each knee type using a paired t-test. The results showed that a polycentric knee performed better than a weight-activated knee. The polycentric knee creates a balance between stability and mobility. The participants with a polycentric knee had a walking speed of 80.74 metres per minute, compared to 68.50 metres per minute with a weight-activated knee (P < 0.05). The symmetry of walking with the polycentric gait was greater than that of the participants with the weight-activated knee, based on the values of gait parameters in time-space. This research will help determine how the asymmetric gait of transfemoral amputees affects their prosthetic knee joints and open the door to better prosthetic components.
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, Bijay Kumar Paikaray International Journal of Reasoning Based Intelligent Systems, 2023 Human brain is considered as the most sophisticated part of the body and used to consist of several neurons and biological components. The normal brain is usually functioning at an aspect of 93% for a healthy human. A brain tumour is a common disease nowadays and this disease usually leads to the accumulation of aberrant cells in certain brain tissues, which may cause the formation of dump cells in the brain. One of the most valuable approaches is the MRI images which can identify the various stages for the detection of the tumour. Here a variety of feature extraction and classification techniques are available and MRI pictures are used to identify brain tumours. Here in this paper, the convolutional neural network approach is discussed where the high-accuracy image classification technique for early tumour detection is used.
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, B. K. Paikaray Eai Endorsed Transactions on Pervasive Health and Technology, 2023 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, Amar Nath Singh 2022 2nd International Conference on Computer Science Engineering and Applications Iccsea 2022, 2022 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.
