Open Access

Abhishek Saigiridhari, Abhishek Mishra, Aarya Tupe, Dhanalekshmi Yedurkar, Manisha Galphade

• Globally, neurological disorders are a major health concern affecting a population of billions worldwide. There’s a need for accurate and timely diagnosis of brain disorders to improve patient outcomes and revolutionize the field of medicine with the help of technology. For this, the integration of deep learning models with MRI (structural and functional) images presents a promising approach for the detection of brain disorders like Alzheimer’s disease. Our Research aims to develop and evaluate deep learning models for detecting Alzheimer’s disease using the Oasis dataset, a popularly used data set of neuroimaging and processed imaging data, for brain images of Alzheimer patients. There were 2 types of images i.e. the Raw and FSL-SEG (preprocessed) gifs. The models were developed using multiple Convolution layers and a Non-linear activation function (Sigmoid) for binary classification. Early stopping on loss helped prevent overfitting, and a batch size of 75 was used for fasterGlobally, neurological disorders are a major health concern affecting a population of billions worldwide. There’s a need for accurate and timely diagnosis of brain disorders to improve patient outcomes and revolutionize the field of medicine with the help of technology. For this, the integration of deep learning models with MRI (structural and functional) images presents a promising approach for the detection of brain disorders like Alzheimer’s disease. Our Research aims to develop and evaluate deep learning models for detecting Alzheimer’s disease using the Oasis dataset, a popularly used data set of neuroimaging and processed imaging data, for brain images of Alzheimer patients. There were 2 types of images i.e. the Raw and FSL-SEG (preprocessed) gifs. The models were developed using multiple Convolution layers and a Non-linear activation function (Sigmoid) for binary classification. Early stopping on loss helped prevent overfitting, and a batch size of 75 was used for faster convergence. We generated an accuracy of 90% on the FSL-SEG MRI images whereas the RAW images resulted in an accuracy of 83%. With a value of 0.79 in Area Under the Curve, The CDR (Clinical Dementia Rating) as well as MMSE (Mini Mental State Examination) were main factors which interlinked the images with occurence of Alzheimer.…