Int J Performability Eng ›› 2023, Vol. 19 ›› Issue (10): 676-686.doi: 10.23940/ijpe.23.10.p5.676686
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Sachin Jain* and Vishal Jain
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* E-mail address: Sachin Jain and Vishal Jain. Ensemble Techniques for Classification of Brain Tumor Images Based on Weighting Average of Various Deep Learning-Based Components Models [J]. Int J Performability Eng, 2023, 19(10): 676-686.
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| [1] Khan H.A., Jue W., Mushtaq M. and Mushtaq M.U.Brain Tumor Classification in MRI Image using Convolutional Neural Network. [2] Abd-Ellah, M.K., Awad, A.I., Khalaf, A.A., and Hamed, H.F. A Review on Brain Tumor Diagnosis from MRI Images: Practical Implications, Key Achievements, and Lessons Learned. [3] Logeswari, T. and Karnan, M.An Improved Implementation of Brain Tumor Detection using Segmentation based on Hierarchical Self Organizing Map. [4] Abd-Ellah, M.K., Awad, A.I., Khalaf, A.A., and Hamed, H.F. Classification of Brain Tumor MRIs using a Kernel Support Vector Machine. In Building Sustainable Health Ecosystems: 6th International Conference on Well-Being in the Information Society, Springer International Publishing, pp. 151-160, 2016. [5] Deshpande A., Estrela V.V., andPatavardhan P.The DCT-CNN-ResNet50 Architecture to Classify Brain Tumors with Super-Resolution, Convolutional Neural Network, and the ResNet50. [6] Ayadi W., Elhamzi W., Charfi I., andAtri M.Deep CNN for Brain Tumor Classification. pp. 671-700, 2021. [7] Pashaei A., Sajedi H., andJazayeri N.Brain Tumor Classification via Convolutional Neural Network and Extreme Learning Machines. In [8] Sridhar C., Pareek P.K., Kalidoss R., Jamal S.S., Shukla P.K., andNuagah S.J.Optimal Medical Image Size Reduction Model Creation using Recurrent Neural Network and GenPSOWVQ. [9] Kumarganesh, S. and Suganthi, M.An Efficient Approach for Brain Image (Tissue) Compression based on the Position of the Brain Tumor. [10] Sert E., Özyurt F., andDoğantekin A.A New Approach for Brain Tumor Diagnosis System: Single Image Super Resolution Based Maximum Fuzzy Entropy Segmentation and Convolutional Neural Network. [11] Anjali, R. and Priya, S.An Efficient Classifier for Brain Tumor Classification. [12] Kumar, A., Ashok, A. and Ansari, M.A.Brain Tumor Classification using Hybrid Model of PSO and SVM Classifier. In [13] Song G., Huang Z., Zhao Y., Zhao X., Liu Y., Bao M., Han J., andLi P.A Noninvasive System for the Automatic Detection of Gliomas based on Hybrid Features and PSO-KSVM. [14] Kaplan K., Kaya Y., Kuncan M., andErtunç H.M.Brain Tumor Classification using Modified Local Binary Patterns (LBP) Feature Extraction Methods. [15] Shakeel, P.M., Tobely, T.E.E., Al-Feel, H., Manogaran, G., and Baskar, S. Neural Network Based Brain Tumor Detection using Wireless Infrared Imaging Sensor. [16] Hashemzehi, R., Mahdavi, S.J.S., Kheirabadi, M., and Kamel, S.R. Detection of Brain Tumors from MRI Images Base on Deep Learning using Hybrid Model CNN and NADE. [17] Minz, A. and Mahobiya, C.MR Image Classification using Adaboost for Brain Tumor Type. In [18] Amin J., Sharif M., Yasmin M., andFernandes S.L.A Distinctive Approach in Brain Tumor Detection and Classification using MRI. [19] Cheng Y., Qin G., Zhao R., Liang Y., andSun M.ConvCaps: Multi-Input Capsule Network for Brain Tumor Classification. In Neural Information Processing: 26th International Conference, Springer International Publishing, pp. 524-534, 2019. [20] Bahadure N.B., Ray A.K., andThethi H.P.Image Analysis for MRI Based Brain Tumor Detection and Feature Extraction using Biologically Inspired BWT and SVM. [21] Chaudhary, A. and Bhattacharjee, V.An Efficient Method for Brain Tumor Detection and Categorization using MRI Images by K-Means Clustering & DWT. [22] Mallick P.K., Ryu S.H., Satapathy S.K., Mishra S., Nguyen G.N., andTiwari P.Brain MRI Image Classification for Cancer Detection using Deep Wavelet Autoencoder-Based Deep Neural Network. [23] Kumar, P. and VijayKumar, B. Brain Tumor MRI Segmentation and Classification using Ensemble Classifier. [24] Sriramakrishnan P., Kalaiselvi T., andRajeswaran R.Modified Local Ternary Patterns Technique for Brain Tumour Segmentation and Volume Estimation from MRI Multi-Sequence Scans with GPU CUDA Machine. [25] Marghalani, B.F. and Arif, M.Automatic Classification of Brain Tumor and Alzheimer’S Disease in MRI. [26] Deepak, S. and Ameer, P.M.Brain Tumor Classification using Deep CNN Features via Transfer Learning. [27] Ghassemi N., Shoeibi A., andRouhani M.Deep Neural Network with Generative Adversarial Networks Pre-Training for Brain Tumor Classification based on MR Images. [28] Vimal Kurup, R., Sowmya, V., and Soman, K.P. Effect of Data Pre-Processing on Brain Tumor Classification using Capsulenet. In [29] Arasi P.R.E. and Suganthi, M.A Clinical Support System for Brain Tumor Classification using Soft Computing Techniques. [30] Chandra, S.K. and Bajpai, M.K.Fractional Mesh-Free Linear Diffusion Method for Image Enhancement and Segmentation for Automatic Tumor Classification. [31] Raja P.S.Brain Tumor Classification using a Hybrid Deep Autoencoder with Bayesian Fuzzy Clustering-Based Segmentation Approach. [32] Hamid, M.A. and Khan, N.A.Investigation and Classification of MRI Brain Tumors using Feature Extraction Technique. [33] Çinar, A. and Yildirim, M.Detection of Tumors on Brain MRI Images using the Hybrid Convolutional Neural Network Architecture. [34] Begum, S.S. and Lakshmi, D.R.Combining Optimal Wavelet Statistical Texture and Recurrent Neural Network for Tumour Detection and Classification Over MRI. [35] Eluri V.R., Ramesh C., Dhipti S.N., andSujatha D.Analysis of MRI-Based Brain Tumor Detection using RFCM Clustering and SVM Classifier. In [36] Bakas S.S.BraTS MICCAI Brain Tumor Dataset.IEEE Dataport, 2020. |
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