Gopal Singh Tandel
@vitbhopal.ac.in
Assistant Professor
VIT Bhopal University
I am currently working as Assistant Professor at VIT Bhopal University. I have completed a Ph.D. (Full Time) degree from the Department of Computer Science and Engineering, Visvesvaraya National Institute of Technology, Nagpur. So far, I have published Five SCI-indexed papers, and their citation score is 302+. I obtained BE and MTech degrees in Computer Science and Engineering in the year June-2005 and Dec-2008, respectively from RGPV University Bhopal (M.P.). I started my career in education in August 2005 and worked in various esteemed educational institutes such as SV Polytechnic Bhopal, LNCT Bhopal, AITR Bhopal, NIIST Bhopal, OP Jindal University Raigarh (Chhattishgarh), and VNIT Nagpur, I have 17+ years of experience which includes 13 years of teaching and 4+ years of research. My area of interest is to create a conducive environment for imparting quality education with the spirit of service to the nation and humanity. I believe in motivating students to achieve excellence in educ
RESEARCH INTERESTS
Health care, deep learning , Machine learning, Digital Image Processing
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Gopal S. Tandel, Ashish Tiwari, Omprakash G. Kakde, Neha Gupta, Luca Saba, and Jasjit S. Suri
MDPI AG
The biopsy is a gold standard method for tumor grading. However, due to its invasive nature, it has sometimes proved fatal for brain tumor patients. As a result, a non-invasive computer-aided diagnosis (CAD) tool is required. Recently, many magnetic resonance imaging (MRI)-based CAD tools have been proposed for brain tumor grading. The MRI has several sequences, which can express tumor structure in different ways. However, a suitable MRI sequence for brain tumor classification is not yet known. The most common brain tumor is ‘glioma’, which is the most fatal form. Therefore, in the proposed study, to maximize the classification ability between low-grade versus high-grade glioma, three datasets were designed comprising three MRI sequences: T1-Weighted (T1W), T2-weighted (T2W), and fluid-attenuated inversion recovery (FLAIR). Further, five well-established convolutional neural networks, AlexNet, VGG16, ResNet18, GoogleNet, and ResNet50 were adopted for tumor classification. An ensemble algorithm was proposed using the majority vote of above five deep learning (DL) models to produce more consistent and improved results than any individual model. Five-fold cross validation (K5-CV) protocol was adopted for training and testing. For the proposed ensembled classifier with K5-CV, the highest test accuracies of 98.88 ± 0.63%, 97.98 ± 0.86%, and 94.75 ± 0.61% were achieved for FLAIR, T2W, and T1W-MRI data, respectively. FLAIR-MRI data was found to be most significant for brain tumor classification, where it showed a 4.17% and 0.91% improvement in accuracy against the T1W-MRI and T2W-MRI sequence data, respectively. The proposed ensembled algorithm (MajVot) showed significant improvements in the average accuracy of three datasets of 3.60%, 2.84%, 1.64%, 4.27%, and 1.14%, respectively, against AlexNet, VGG16, ResNet18, GoogleNet, and ResNet50.
Gopal S. Tandel, Ashish Tiwari, and O.G. Kakde
Elsevier BV
Gopal S. Tandel, Ashish Tiwari, and O.G. Kakde
Elsevier BV
Jasjit S. Suri, Sushant Agarwal, Suneet K. Gupta, Anudeep Puvvula, Mainak Biswas, Luca Saba, Arindam Bit, Gopal S. Tandel, Mohit Agarwal, Anubhav Patrick,et al.
Elsevier BV
Gopal S. Tandel, Antonella Balestrieri, Tanay Jujaray, Narender N. Khanna, Luca Saba, and Jasjit S. Suri
Elsevier BV
Gopal S. Tandel, Mainak Biswas, Omprakash G. Kakde, Ashish Tiwari, Harman S. Suri, Monica Turk, John Laird, Christopher Asare, Annabel A. Ankrah, N. N. Khanna,et al.
MDPI AG
A World Health Organization (WHO) Feb 2018 report has recently shown that mortality rate due to brain or central nervous system (CNS) cancer is the highest in the Asian continent. It is of critical importance that cancer be detected earlier so that many of these lives can be saved. Cancer grading is an important aspect for targeted therapy. As cancer diagnosis is highly invasive, time consuming and expensive, there is an immediate requirement to develop a non-invasive, cost-effective and efficient tools for brain cancer characterization and grade estimation. Brain scans using magnetic resonance imaging (MRI), computed tomography (CT), as well as other imaging modalities, are fast and safer methods for tumor detection. In this paper, we tried to summarize the pathophysiology of brain cancer, imaging modalities of brain cancer and automatic computer assisted methods for brain cancer characterization in a machine and deep learning paradigm. Another objective of this paper is to find the current issues in existing engineering methods and also project a future paradigm. Further, we have highlighted the relationship between brain cancer and other brain disorders like stroke, Alzheimer’s, Parkinson’s, and Wilson’s disease, leukoriaosis, and other neurological disorders in the context of machine learning and the deep learning paradigm.
RECENT SCHOLAR PUBLICATIONS
MOST CITED SCHOLAR PUBLICATIONS
Publications
1. Tandel, G.S., Tiwari, A. and Kakde, O.G., (2022). Performance enhancement of MRI-based brain tumor classification using suitable segmentation method and deep learning-based ensemble algorithm. Biomedical Signal Processing and Control, 78, p.104018. (SCIE, IF:5.076), DOI:
2. Tandel, G.S., Tiwari, A. and Kakde, O.G., (2021). Performance Optimisation of Deep Learning Models using Majority Voting Algorithm for Brain Tumour Classification. Computers in Biology and Medicine, p.104564. (SCI, IF:4.59),
DOI:
3. Suri, J.S., Agarwal, S., Gupta, S.K., Puvvula, A., Biswas, M., Saba, L., Bit, A., Tandel, G.S., Agarwal, M., Patrick, A. and Faa, G., (2021). A narrative review on characterization of acute respiratory distress syndrome in COVID-19-infected lungs using artificial intelligence. Computers in Biology and Medicine, p.104210. (SCI, IF:4.59),
DOI:
4. Tandel, G.S., Balestrieri, A., Jujaray, T., Khanna, N.N., Saba, L. and Suri, J.S., (2020). Multiclass magnetic resonance imaging brain tumor classification using artificial intelligence paradigm. Computers in Biology and Medicine, 122, p.103804. (SCI, IF:4.59), DOI:
5. Tandel, G.S., Biswas, M., Kakde, O.G., Tiwari, A., Suri, H.S., Turk, M., Laird, J.R., Asare, C.K., Ankrah, A.A., Khanna, N.N. and Madhusudhan, B.K., (2019). A review on a deep learn