BRAIN TUMOR DETECTION AND CLASSIFICATION USING DEEP FEATURE FUSION AND STACKING CONCEPTS
Main Article Content
Keywords
transfer learning, deep learning, ensemble learning, brain tumor classification, PCA, machine learning
Abstract
The Classification of brain tumors plays an important role in determining the treatment plan, course of therapy and survival rate. A new technique is proposed in this work for classification of brain tumors based on pre-trained neural networks and a stacking algorithm. Then our method begins with drawing multiple pre-train CNNs on T1 weighted images of MR brain scans where it extracts features from these. Afterwards, an ensemble of these features are used as input to a single layer stacking algorithm, which stacks together the predictions of several base classifiers to arrive at the final prediction. We evaluate our method on two publicly available datasets of brain MRI scans and show it can detect lesions with superior accuracy compared to other methods. In our approach using a pre-trained CNN allows us to leverage the transfer learning concept because the CNN had been trained in advance on a huge image database and extracted relevant features for the task of classifying brain tumors. An enhanced accuracy is achieved through a combination of various base classifiers with a stacking algorithm. The results of our study demonstrate that we have a promising method of categorizing brain tumors and improving healthcare provision.
References
1. Verkhratsky, A.; Nedergaard, M. The homeostatic astroglia emerges from evolutionary specialization of neural cells. Philosophical Transactions of the Royal Society B: Biological Sciences 2016, 371, 20150428.
2. Muhammad, K.; Khan, S.; Del Ser, J.; De Albuquerque, V.H.C. Deep learning for multigrade brain tumor classification in smart healthcare systems: A prospective survey. IEEE Transactions on Neural Networks and Learning Systems 2020, 32, 507-522.
3. Muzammal, M.; Talat, R.; Sodhro, A.H.; Pirbhulal, S. A multi-sensor data fusion enabled ensemble approach for medical data from body sensor networks. Information Fusion 2020, 53, 155-164.
4. Pirbhulal, S.; Wu, W.; Mukhopadhyay, S.C.; Li, G. Adaptive energy optimization algorithm for internet of medical things. In Proceedings of the 2018 12th International Conference on Sensing Technology (ICST), 2018; pp. 269-272.
5. Zhang, H.; Zhang, H.; Pirbhulal, S.; Wu, W.; Albuquerque, V.H.C.D. Active balancing mechanism for imbalanced medical data in deep learning–based classification models. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 2020, 16, 1-15.
6. Zhang, T.; Sodhro, A.H.; Luo, Z.; Zahid, N.; Nawaz, M.W.; Pirbhulal, S.; Muzammal, M. A joint deep learning and internet of medical things driven framework for elderly patients. IEEE Access 2020, 8, 75822-75832.
7. Rehman, A.; Naz, S.; Razzak, M.I.; Akram, F.; Imran, M. A deep learning-based framework for automatic brain tumors classification using transfer learning. Circuits, Systems, and Signal Processing 2020, 39, 757-775.
8. Ahmed, N., Hussain, M., & Malik, A. . Brain tumor segmentation using a hybrid approach of wavelet transform and gray-level co-occurrence matrix. . Journal of Medical Imaging and Health Informatics, 2015, 5(6), 1246-1253.
9. Cheng, J.; Huang, W.; Cao, S.; Yang, R.; Yang, W.; Yun, Z.; Wang, Z.; Feng, Q. Enhanced performance of brain tumor classification via tumor region augmentation and partition. PloS one 2015, 10, e0140381.
10. Kumar, S.; Dabas, C.; Godara, S. Classification of brain MRI tumor images: a hybrid approach. Procedia computer science 2017, 122, 510-517.
11. Bahadure, N.B.; Ray, A.K.; Thethi, H.P. Image Analysis for MRI Based Brain Tumor Detection and Feature Extraction Using Biologically Inspired BWT and SVM. International Journal of Biomedical Imaging 2017, 2017, 9749108, doi:10.1155/2017/9749108.
12. Jiang, Y., Chen, C., & Zhou, Y. . Brain tumor classification using T1-weighted MRI scans based on region-based active contour model and random forest algorithm. . Journal of Medical Imaging and Health Informatics 2021, 11(4), 797-804.
13. Yao, J., Xie, B., Ma, H., Jin, M., & Yang, X. . Brain Tumor Classification Using Fuzzy Cognitive Maps Based on MRI Data. Journal of Healthcare Engineering 2020.
14. Deepa, S.; Devi, B.A. Artificial neural networks design for classification of brain tumour. In Proceedings of the 2012 International Conference on Computer Communication and Informatics, 2012; pp. 1-6.
15. Khened, M., Kori, A. V., & Sonawane, R. S. Brain tumor segmentation using deep learning. International Journal of Advanced Research in Computer Science 2019, 10(5), 86-90.
16. Soltaninejad, M.; Yang, G.; Lambrou, T.; Allinson, N.; Jones, T.L.; Barrick, T.R.; Howe, F.A.; Ye, X. Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI. International journal of computer assisted radiology and surgery 2017, 12, 183-203.
17. Li, Y., Jiang, B., Ma, Z., & Yang, X. A hybrid approach of traditional and automated learning methods for brain MRI images classification. Journal of medical systems 2021, 45(7), 73.
18. Rosebrock, A. Finding Extreme Points in Contours with OpenCV. Available online: https://pyimagesearch.com/2016/04/11/finding-extreme-points-in-contours-with-opencv/ (accessed on
19. Yang, S.; Xiao, W.; Zhang, M.; Guo, S.; Zhao, J.; Shen, F. Image Data Augmentation for Deep Learning: A Survey. arXiv preprint arXiv:2204.08610 2022.
20. Sundararajan, S.K.; Sankaragomathi, B.; Priya, D.S. Deep Belief CNN Feature Representation Based Content Based Image Retrieval for Medical Images. Journal of Medical Systems 2019, 43, 174, doi:10.1007/s10916-019-1305-6.
21. Thomas, C. An introduction to Convolutional Neural Networks. 2019, 2022.
22. Albawi, S.; Mohammed, T.A.; Al-Zawi, S. Understanding of a convolutional neural network. In Proceedings of the 2017 international conference on engineering and technology (ICET), 2017; pp. 1-6.
23. Indolia, S.; Goswami, A.K.; Mishra, S.P.; Asopa, P. Conceptual understanding of convolutional neural network-a deep learning approach. Procedia computer science 2018, 132, 679-688.
24. Zhuang, F.; Qi, Z.; Duan, K.; Xi, D.; Zhu, Y.; Zhu, H.; Xiong, H.; He, Q. A comprehensive survey on transfer learning. Proceedings of the IEEE 2020, 109, 43-76.
25. Weiss, K.; Khoshgoftaar, T.M.; Wang, D. A survey of transfer learning. Journal of Big Data 2016, 3, 9, doi:10.1186/s40537-016-0043-6.
26. Tan, C.; Sun, F.; Kong, T.; Zhang, W.; Yang, C.; Liu, C. A Survey on Deep Transfer Learning. In Proceedings of the Artificial Neural Networks and Machine Learning – ICANN 2018, Cham, 2018//, 2018; pp. 270-279.
27. Salahat, E.; Qasaimeh, M. Recent advances in features extraction and description algorithms: A comprehensive survey. In Proceedings of the 2017 IEEE International Conference on Industrial Technology (ICIT), 22-25 March 2017, 2017; pp. 1059-1063.
28. Szegedy, C.; Ioffe, S.; Vanhoucke, V.; Alemi, A.A. Inception-v4, inception-resnet and the impact of residual connections on learning. In Proceedings of the Thirty-first AAAI conference on artificial intelligence, 2017.
29. Huang, G.; Liu, Z.; Van Der Maaten, L.; Weinberger, K.Q. Densely connected convolutional networks. In Proceedings of the Proceedings of the IEEE conference on computer vision and pattern recognition, 2017; pp. 4700-4708.
30. Simonyan, K.; Zisserman, A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 2014.
31. Szegedy, C.; Vanhoucke, V.; Ioffe, S.; Shlens, J.; Wojna, Z. Rethinking the inception architecture for computer vision. In Proceedings of the Proceedings of the IEEE conference on computer vision and pattern recognition, 2016; pp. 2818-2826.
32. Zoph, B.; Vasudevan, V.; Shlens, J.; Le, Q.V. Learning transferable architectures for scalable image recognition. In Proceedings of the Proceedings of the IEEE conference on computer vision and pattern recognition, 2018; pp. 8697-8710.
33. Sandler, M.; Howard, A.; Zhu, M.; Zhmoginov, A.; Chen, L.-C. Mobilenetv2: Inverted residuals and linear bottlenecks. In Proceedings of the Proceedings of the IEEE conference on computer vision and pattern recognition, 2018; pp. 4510-4520.
34. Howard, A.G.; Zhu, M.; Chen, B.; Kalenichenko, D.; Wang, W.; Weyand, T.; Andreetto, M.; Adam, H. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 2017.
35. Jolliffe Ian T. and Cadima Jorge. Principal component analysis: a review and recent developments. The Royal Society 13 April 2016, Volume 374, doi:https://doi.org/10.10 98/rsta. 2015.0202.
36. Huang, G.-B.; Zhu, Q.-Y.; Siew, C.-K. Extreme learning machine: Theory and applications. Neurocomputing 2006, 70, 489-501, doi:https://doi.org/10.1016/j.neucom.2005.12.126.
37. Zhang, J.; Ding, W. Prediction of air pollutants concentration based on an extreme learning machine: the case of Hong Kong. International journal of environmental research and public health 2017, 14, 114.
38. Parvathy Jyothi , R.A.S. Classification of Tumors from Brain MR Slices using PCA for Discriminative Models. INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY 14 December 2021, 10, doi:10.17577/IJERTV10IS120053.
39. Pedregosa, F.; Varoquaux, G.; Gramfort, A.; Michel, V.; Thirion, B.; Grisel, O.; Blondel, M.; Prettenhofer, P.; Weiss, R.; Dubourg, V. Scikit-learn: Machine learning in Python. the Journal of machine Learning research 2011, 12, 2825-2830.
40. Freund, Y.; Schapire, R.E. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of computer and system sciences 1997, 55, 119-139.
41. Hastie, T.; Rosset, S.; Zhu, J.; Zou, H. Multi-class adaboost. Statistics and its Interface 2009, 2, 349-360.
42. Alpaydin, E. Introduction to machine learning 2nd ed. 2010.
43. Zhang, M.-L.; Zhou, Z.-H. ML-KNN: A lazy learning approach to multi-label learning. Pattern recognition 2007, 40, 2038-2048.
44. Breiman, L. Random forests. Machine learning 2001, 45, 5-32.
45. Schintler, L.A.; McNeely, C.L. Encyclopedia of big data; Springer: 2019.
46. Tang, B.; Pan, Z.; Yin, K.; Khateeb, A. Recent advances of deep learning in bioinformatics and computational biology. Frontiers in genetics 2019, 10, 214.
47. Cortes, C.; Vapnik, V. Support-vector networks. Machine learning 1995, 20, 273-297.
48. Abdar, M.; Acharya, U.R.; Sarrafzadegan, N.; Makarenkov, V. NE-nu-SVC: a new nested ensemble clinical decision support system for effective diagnosis of coronary artery disease. IEEE Access 2019, 7, 167605-167620.
49. Chen, T.; Guestrin, C. Xgboost: A scalable tree boosting system. In Proceedings of the Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, 2016; pp. 785-794.
50. Kang, S.; Cho, S.; Kang, P. Multi-class classification via heterogeneous ensemble of one-class classifiers. Engineering Applications of Artificial Intelligence 2015, 43, 35-43, doi:https://doi.org/10.1016/j.engappai.2015.04.003.
51. Ranjan, G.; Verma, A.K.; Radhika, S. K-nearest neighbors and grid search cv based real time fault monitoring system for industries. In Proceedings of the 2019 IEEE 5th international conference for convergence in technology (I2CT), 2019; pp. 1-5.
52. Panca, V.; Rustam, Z. Application of machine learning on brain cancer multiclass classification. In Proceedings of the AIP Conference Proceedings, 2017; p. 030133.
53. CHAKRABARTY, N. Brain MRI Images for Brain Tumor Detection. Available online: https://www.kaggle.com/datasets/navoneel/brain-mri-images-for-brain-tumor-detection (accessed on
54. HAMADA, A. Br35H:: Brain Tumor Detection 2020. Available online:
https://www.kaggle.com/datasets/ahmedhamada0/brain-tumor-detection (accessed on
2. Muhammad, K.; Khan, S.; Del Ser, J.; De Albuquerque, V.H.C. Deep learning for multigrade brain tumor classification in smart healthcare systems: A prospective survey. IEEE Transactions on Neural Networks and Learning Systems 2020, 32, 507-522.
3. Muzammal, M.; Talat, R.; Sodhro, A.H.; Pirbhulal, S. A multi-sensor data fusion enabled ensemble approach for medical data from body sensor networks. Information Fusion 2020, 53, 155-164.
4. Pirbhulal, S.; Wu, W.; Mukhopadhyay, S.C.; Li, G. Adaptive energy optimization algorithm for internet of medical things. In Proceedings of the 2018 12th International Conference on Sensing Technology (ICST), 2018; pp. 269-272.
5. Zhang, H.; Zhang, H.; Pirbhulal, S.; Wu, W.; Albuquerque, V.H.C.D. Active balancing mechanism for imbalanced medical data in deep learning–based classification models. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 2020, 16, 1-15.
6. Zhang, T.; Sodhro, A.H.; Luo, Z.; Zahid, N.; Nawaz, M.W.; Pirbhulal, S.; Muzammal, M. A joint deep learning and internet of medical things driven framework for elderly patients. IEEE Access 2020, 8, 75822-75832.
7. Rehman, A.; Naz, S.; Razzak, M.I.; Akram, F.; Imran, M. A deep learning-based framework for automatic brain tumors classification using transfer learning. Circuits, Systems, and Signal Processing 2020, 39, 757-775.
8. Ahmed, N., Hussain, M., & Malik, A. . Brain tumor segmentation using a hybrid approach of wavelet transform and gray-level co-occurrence matrix. . Journal of Medical Imaging and Health Informatics, 2015, 5(6), 1246-1253.
9. Cheng, J.; Huang, W.; Cao, S.; Yang, R.; Yang, W.; Yun, Z.; Wang, Z.; Feng, Q. Enhanced performance of brain tumor classification via tumor region augmentation and partition. PloS one 2015, 10, e0140381.
10. Kumar, S.; Dabas, C.; Godara, S. Classification of brain MRI tumor images: a hybrid approach. Procedia computer science 2017, 122, 510-517.
11. Bahadure, N.B.; Ray, A.K.; Thethi, H.P. Image Analysis for MRI Based Brain Tumor Detection and Feature Extraction Using Biologically Inspired BWT and SVM. International Journal of Biomedical Imaging 2017, 2017, 9749108, doi:10.1155/2017/9749108.
12. Jiang, Y., Chen, C., & Zhou, Y. . Brain tumor classification using T1-weighted MRI scans based on region-based active contour model and random forest algorithm. . Journal of Medical Imaging and Health Informatics 2021, 11(4), 797-804.
13. Yao, J., Xie, B., Ma, H., Jin, M., & Yang, X. . Brain Tumor Classification Using Fuzzy Cognitive Maps Based on MRI Data. Journal of Healthcare Engineering 2020.
14. Deepa, S.; Devi, B.A. Artificial neural networks design for classification of brain tumour. In Proceedings of the 2012 International Conference on Computer Communication and Informatics, 2012; pp. 1-6.
15. Khened, M., Kori, A. V., & Sonawane, R. S. Brain tumor segmentation using deep learning. International Journal of Advanced Research in Computer Science 2019, 10(5), 86-90.
16. Soltaninejad, M.; Yang, G.; Lambrou, T.; Allinson, N.; Jones, T.L.; Barrick, T.R.; Howe, F.A.; Ye, X. Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI. International journal of computer assisted radiology and surgery 2017, 12, 183-203.
17. Li, Y., Jiang, B., Ma, Z., & Yang, X. A hybrid approach of traditional and automated learning methods for brain MRI images classification. Journal of medical systems 2021, 45(7), 73.
18. Rosebrock, A. Finding Extreme Points in Contours with OpenCV. Available online: https://pyimagesearch.com/2016/04/11/finding-extreme-points-in-contours-with-opencv/ (accessed on
19. Yang, S.; Xiao, W.; Zhang, M.; Guo, S.; Zhao, J.; Shen, F. Image Data Augmentation for Deep Learning: A Survey. arXiv preprint arXiv:2204.08610 2022.
20. Sundararajan, S.K.; Sankaragomathi, B.; Priya, D.S. Deep Belief CNN Feature Representation Based Content Based Image Retrieval for Medical Images. Journal of Medical Systems 2019, 43, 174, doi:10.1007/s10916-019-1305-6.
21. Thomas, C. An introduction to Convolutional Neural Networks. 2019, 2022.
22. Albawi, S.; Mohammed, T.A.; Al-Zawi, S. Understanding of a convolutional neural network. In Proceedings of the 2017 international conference on engineering and technology (ICET), 2017; pp. 1-6.
23. Indolia, S.; Goswami, A.K.; Mishra, S.P.; Asopa, P. Conceptual understanding of convolutional neural network-a deep learning approach. Procedia computer science 2018, 132, 679-688.
24. Zhuang, F.; Qi, Z.; Duan, K.; Xi, D.; Zhu, Y.; Zhu, H.; Xiong, H.; He, Q. A comprehensive survey on transfer learning. Proceedings of the IEEE 2020, 109, 43-76.
25. Weiss, K.; Khoshgoftaar, T.M.; Wang, D. A survey of transfer learning. Journal of Big Data 2016, 3, 9, doi:10.1186/s40537-016-0043-6.
26. Tan, C.; Sun, F.; Kong, T.; Zhang, W.; Yang, C.; Liu, C. A Survey on Deep Transfer Learning. In Proceedings of the Artificial Neural Networks and Machine Learning – ICANN 2018, Cham, 2018//, 2018; pp. 270-279.
27. Salahat, E.; Qasaimeh, M. Recent advances in features extraction and description algorithms: A comprehensive survey. In Proceedings of the 2017 IEEE International Conference on Industrial Technology (ICIT), 22-25 March 2017, 2017; pp. 1059-1063.
28. Szegedy, C.; Ioffe, S.; Vanhoucke, V.; Alemi, A.A. Inception-v4, inception-resnet and the impact of residual connections on learning. In Proceedings of the Thirty-first AAAI conference on artificial intelligence, 2017.
29. Huang, G.; Liu, Z.; Van Der Maaten, L.; Weinberger, K.Q. Densely connected convolutional networks. In Proceedings of the Proceedings of the IEEE conference on computer vision and pattern recognition, 2017; pp. 4700-4708.
30. Simonyan, K.; Zisserman, A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 2014.
31. Szegedy, C.; Vanhoucke, V.; Ioffe, S.; Shlens, J.; Wojna, Z. Rethinking the inception architecture for computer vision. In Proceedings of the Proceedings of the IEEE conference on computer vision and pattern recognition, 2016; pp. 2818-2826.
32. Zoph, B.; Vasudevan, V.; Shlens, J.; Le, Q.V. Learning transferable architectures for scalable image recognition. In Proceedings of the Proceedings of the IEEE conference on computer vision and pattern recognition, 2018; pp. 8697-8710.
33. Sandler, M.; Howard, A.; Zhu, M.; Zhmoginov, A.; Chen, L.-C. Mobilenetv2: Inverted residuals and linear bottlenecks. In Proceedings of the Proceedings of the IEEE conference on computer vision and pattern recognition, 2018; pp. 4510-4520.
34. Howard, A.G.; Zhu, M.; Chen, B.; Kalenichenko, D.; Wang, W.; Weyand, T.; Andreetto, M.; Adam, H. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 2017.
35. Jolliffe Ian T. and Cadima Jorge. Principal component analysis: a review and recent developments. The Royal Society 13 April 2016, Volume 374, doi:https://doi.org/10.10 98/rsta. 2015.0202.
36. Huang, G.-B.; Zhu, Q.-Y.; Siew, C.-K. Extreme learning machine: Theory and applications. Neurocomputing 2006, 70, 489-501, doi:https://doi.org/10.1016/j.neucom.2005.12.126.
37. Zhang, J.; Ding, W. Prediction of air pollutants concentration based on an extreme learning machine: the case of Hong Kong. International journal of environmental research and public health 2017, 14, 114.
38. Parvathy Jyothi , R.A.S. Classification of Tumors from Brain MR Slices using PCA for Discriminative Models. INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY 14 December 2021, 10, doi:10.17577/IJERTV10IS120053.
39. Pedregosa, F.; Varoquaux, G.; Gramfort, A.; Michel, V.; Thirion, B.; Grisel, O.; Blondel, M.; Prettenhofer, P.; Weiss, R.; Dubourg, V. Scikit-learn: Machine learning in Python. the Journal of machine Learning research 2011, 12, 2825-2830.
40. Freund, Y.; Schapire, R.E. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of computer and system sciences 1997, 55, 119-139.
41. Hastie, T.; Rosset, S.; Zhu, J.; Zou, H. Multi-class adaboost. Statistics and its Interface 2009, 2, 349-360.
42. Alpaydin, E. Introduction to machine learning 2nd ed. 2010.
43. Zhang, M.-L.; Zhou, Z.-H. ML-KNN: A lazy learning approach to multi-label learning. Pattern recognition 2007, 40, 2038-2048.
44. Breiman, L. Random forests. Machine learning 2001, 45, 5-32.
45. Schintler, L.A.; McNeely, C.L. Encyclopedia of big data; Springer: 2019.
46. Tang, B.; Pan, Z.; Yin, K.; Khateeb, A. Recent advances of deep learning in bioinformatics and computational biology. Frontiers in genetics 2019, 10, 214.
47. Cortes, C.; Vapnik, V. Support-vector networks. Machine learning 1995, 20, 273-297.
48. Abdar, M.; Acharya, U.R.; Sarrafzadegan, N.; Makarenkov, V. NE-nu-SVC: a new nested ensemble clinical decision support system for effective diagnosis of coronary artery disease. IEEE Access 2019, 7, 167605-167620.
49. Chen, T.; Guestrin, C. Xgboost: A scalable tree boosting system. In Proceedings of the Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, 2016; pp. 785-794.
50. Kang, S.; Cho, S.; Kang, P. Multi-class classification via heterogeneous ensemble of one-class classifiers. Engineering Applications of Artificial Intelligence 2015, 43, 35-43, doi:https://doi.org/10.1016/j.engappai.2015.04.003.
51. Ranjan, G.; Verma, A.K.; Radhika, S. K-nearest neighbors and grid search cv based real time fault monitoring system for industries. In Proceedings of the 2019 IEEE 5th international conference for convergence in technology (I2CT), 2019; pp. 1-5.
52. Panca, V.; Rustam, Z. Application of machine learning on brain cancer multiclass classification. In Proceedings of the AIP Conference Proceedings, 2017; p. 030133.
53. CHAKRABARTY, N. Brain MRI Images for Brain Tumor Detection. Available online: https://www.kaggle.com/datasets/navoneel/brain-mri-images-for-brain-tumor-detection (accessed on
54. HAMADA, A. Br35H:: Brain Tumor Detection 2020. Available online:
https://www.kaggle.com/datasets/ahmedhamada0/brain-tumor-detection (accessed on
Article Sidebar
Published
Jan 24, 2024
Article Details
How to Cite
Saqlain Raza, Nasim Gul, Haider Ali Khattak, Arisha Rehan, Muhammad Imran Farid, Anum Kamal, Dr Jai Singh Rajput, Sajid Mukhtiar, & Aziz Ullah. (2024). BRAIN TUMOR DETECTION AND CLASSIFICATION USING DEEP FEATURE FUSION AND STACKING CONCEPTS. Journal of Population Therapeutics and Clinical Pharmacology, 31(1), 1339–1356. https://doi.org/10.53555/jptcp.v31i1.4179
Section
Articles
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
All articles published in JPTCP are licensed under Copyright Creative Commons Attribution-NonCommercial 4.0 International License.
Author Biographies
Saqlain Raza
Department of Control Science and Engineering, Xi’an University of Architecture and Technology, Xi’an, Shaanxi 710055, China
Nasim Gul
Specialist Neurosurgery Zayed Military Hospital - Abu Dhabi.
Haider Ali Khattak
Consultant Neurosurgeon, Neurosurgery Department, Ayub Medical Complex, Abbottabad - Pakistan
Arisha Rehan
MBBS Jinnah Sindh Medical University, Karachi - Pakistan
Muhammad Imran Farid
Department of Electrical and Computer Engineering, Air University, Islamabad - Pakistan
Anum Kamal
MBBS Jinnah Sindh Medical University, Karachi - Pakistan
Dr Jai Singh Rajput
Independent Researcher Family Medicine, Montgomery - Alabama
Sajid Mukhtiar
Department of Control Science and Engineering, Xi’an University of Architecture and Technology, Xi’an, Shaanxi 710055, China
Aziz Ullah
IMS University of Science and Technology, Bannu KPK - Pakistan