Advanced Machine Learning for Brain Tumor and Alzheimer’s Disease Detection: A Comprehensive Review of Neuroimaging-based Classification Techniques

Year 2024, , 62 - 68, 30.12.2024

Naman Kumar , Vaibhav Narawade , Kanish Chheda , Harisha Patkar , Aniket Mishra

https://doi.org/10.54569/aair.1582085

Abstract

Alzheimer's disease with progressive neurodegeneration and brain tumors notably characterized by rapid, not limited cell proliferation poses significant health risks unless timely diagnosed and treated. Tumors have a diverse feature and characteristics, added to subtle changes in the brain whose hallmark is Alzheimer's, making accurate segmentation and classification quite challenging. Indeed, while there have been research in the last decade or so that have proven promising results, challenges still linger on. The present work discusses various approaches for image classification and staging of Alzheimer's disease and brain tumors by exploiting techniques in statistical image processing and computational intelligence. This paper includes discussion on morphology of brain tumors along with neuroimaging changes caused by Alzheimer's disease, existing datasets, data augmentation techniques, and methods for component extraction and classification within the DL, TL, and ML framework. Such specific systems have been given the metrics using the datasets; the descriptions of the implementations, however may vary with the case at hand.

Keywords

Alzheimer Detection, Convolutional Neural Networks, Brain Tumor Detection

References

• Yadav, S. S., & Jadhav, S. M. (2019). Deep convolutional neural network based medical image classification for disease diagnosis. Journal of Big Data, 6(1), 1-18.
• Irmak, E. (2021). Multi-Classification of Brain Tumor MRI Images Using Deep Convolutional Neural Network with Fully Optimized Framework. Electronics, 10(2), 184.
• Wen, J., Thibeau-Sutre, E., Diaz-Melo, M., Samper-Gonzalez, J., Routier, A., Bottani, S., ... & Colliot, O. (2020). Convolutional neural networks for classification of Alzheimer's disease: Overview and reproducible evaluation. Medical Image Analysis, 63, 101694.
• Mehmood, A., Yang, S., Feng, Z., Wang, M., Ahmad, A. S., Khan, R., ... & Yaqub, M. (2021). A transfer learning approach for early diagnosis of Alzheimer's disease on MRI images. Neuroscience, 460, 43-52.
• Xie, X. (2021). Deep learning-based image classification of MRI brain image. College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
• Singh, J., Singh, A., Singh, K. K., Lal, B., William, R. A., Turukmane, A. V., & Kumar, A. (2021). Identification of Brain Diseases using Image Classification: A Deep Learning Approach.
• Woźniak, M., Siłka, J., & Wieczorek, M. (2021). Deep neural network correlation learning mechanism for CT brain tumor detection. Neural Computing and Applications, 33(4), 1143-1155.
• Noreen, N., Palaniappan, S., Qayyum, A., Ahmad, I., Imran, M., & Shoaib, M. (2020). A deep learning model based on a concatenation approach for the diagnosis of brain tumor. IEEE Access, 8, 55135-55144.
• Mehrotra, R., Ansari, M. A., Agrawal, R., & Anand, R. S. (2020). A transfer learning approach for AI-based classification of brain tumors. IEEE Access, 8, 41667-41676.
• Ahmad, I., Siddiqi, M. H., Alhujaili, S. F., & Alrowaili, Z. A. (2022). Improving Alzheimer's disease classification in brain MRI images using a neural network model enhanced with PCA and SWLDA. Biomedical Signal Processing and Control, 71, 103186.
• Lu, B., Li, H. X., Chang, Z. K., Li, L., Chen, N. X., Zhu, Z. C., ... & Yan, C. G. (2023). A practical Alzheimer's disease classifier via brain imaging-based deep learning on 85,721 samples. Medical Image Analysis, 83, 102645.
• Santos Bringas, S., Salomón, R., Duque, C., Lage, J. L., Montaña, J. L. (2024). Alzheimer’s Disease Stage Identification Using Deep Learning Models. Fundación Centro Tecnológico de Componentes CTC, Universidad de Cantabria.
• Santos, D. F. Santos, E. (2024). Brain Tumor Detection Using Deep Learning. BRIDGE – Instituto de Tecnologia e Pesquisa, Faculdade Estácio, Brown University.
• Nayak, D. R., Padhy, N., Mallick, P. K., Zymbler, M., Kumar, S. (2024). Brain Tumor Classification Using Dense EfficientNet. School of Engineering and Technology, GIET University, India; Kalinga Institute of Technology, India; South Ural State University, Russia.
• Nassar, S. E., Yasser, I., Amer, H. M., Mohamed, M. A. (2024). A Robust MRI-Based Brain Tumor Classification via a Hybrid Deep Learning Technique. Electronics and Communication Engineering Department, Mansoura University, Egypt.
• Helaly, H. A., Badawy, M., Haikal, A. Y. (2024). Deep Learning Approach for Early Detection of Alzheimer’s Disease. Electrical Engineering Department, Damietta University, Egypt; Computers and Control Systems Engineering Department, Mansoura University, Egypt; Department of Computer Science and Informatics, Taibah University, Saudi Arabia.
• Odusami, M., Maskeliūnas, R., Damaševičius, R., Misra, S. (2024). Explainable Deep-Learning-Based Diagnosis of Alzheimer’s Disease Using Multimodal Input Fusion of PET and MRI Images. Kaunas University of Technology, Lithuania; Silesian University of Technology, Poland; Institute of Energy Technology, Norway.
• Küstner, T., Qin, C., Sun, C., Ning, L., Scannell, C. M. (2024). The Intelligent Imaging Revolution: AI in MRI and MRS Acquisition and Reconstruction. University Hospital of Tuebingen, Imperial College London, University of Missouri-Columbia, Brigham and Women’s Hospital, Eindhoven University of Technology.
• Sharif, M. I., Khan, M. A., Alhussein, M., Aurangzeb, K., Raza, M. (2024). A Decision Support System for Multimodal Brain Tumor Classification Using Deep Learning. Department of Computer Science, COMSATS University Islamabad, Pakistan; Department of Computer Science, HITEC University, Pakistan; Computer Engineering Department, King Saud University, Saudi Arabia.
• Baranwal, S. K., Jaiswal, K., Vaibhav, K., Kumar, A., Srikantaswamy, R. (2024). Performance Analysis of Brain Tumor Image Classification Using CNN and SVM. Department of Electronics and Communication Engineering, R.V. College of Engineering, Bangalore, India.
• Rana, M., Bhushan, M. (2024). Machine Learning and Deep Learning Approach for Medical Image Analysis: Diagnosis to Detection. Department of Computer Science, LNM Institute of Information Technology, Jaipur, India.
• Krishnammal, P. M., Raja, S. S. (2024). Convolutional Neural Network-Based Image Classification and Detection of Abnormalities in MRI Brain Images. Department of Computer Science, PSG College of Technology, Coimbatore, India.
• Tyagi, V. (2024). A Review on Image Classification Techniques to Classify Neurological Disorders of Brain MRI. Department of Computer Science, University of Delhi, India.
• Alzakri, P. J., Koller, M., Thuet, P., Leu, S., Diebo, T., Schwab, F., Lafage, V. (2024). Risk Factors for Proximal Junctional Kyphosis and Proximal Junctional Failure After Spinal Deformity Surgery: A Systematic Review. Department of Orthopedic Surgery, University Hospital of Geneva, Switzerland.