A Study on Various Machine Learning Techniques for the Early Detection of Alzheimer Disease Using Neuro Images

Effective patient care and therapeutic intervention with Alzheimer's disease (AD) depend on early diagnosis. This work uses neuroimaging data to compare several machine learning approaches for improving AD early detection. A variety of approaches were examined, and datasets obtained from ADNI were used to evaluate each approach. The findings demonstrate the resilience of VGG in spotting early indicators of cognitive deterioration, with transfer learning achieving the best accuracy. High performance is also shown by bat-optimized SVM, demonstrating the usefulness of optimization methods in classification problems. While CNN using 18FDG-PET scans as well as Gaussian-filtered CNN on MRI data have great potential, RF is useful because it can handle complicated multimodal data and is resilient against noise, even though its accuracy is slightly lower. The significance of sophisticated machine learning models in the early identification of AD is highlighted by this work. According to the research, combining optimization and transfer learning strategies can significantly increase diagnostic precision. The significance of sophisticated machine learning models in the early identification of AD is highlighted by this work. According to the research, combining optimization and transfer learning strategies can significantly increase diagnostic precision.