Authors:
Safa Jraba
1
;
Mohamed Elleuch
2
;
Hela Ltifi
3
and
Monji Kherallah
4
Affiliations:
1
National School of Electronics and Telecommunications (ENETCom), University of Sfax, Tunisia
;
2
National School of Computer Science (ENSI), University of Manouba, Tunisia
;
3
Faculty of Sciences and Techniques of Sidi Bouzid, University of Kairouan, Tunisia
;
4
Faculty of Sciences, University of Sfax, Tunisia
Keyword(s):
YOLO, Deep Learning, Early Diagnosis, MRI, Medical Imaging, Detection, Brain Imaging, YOLOv8.
Abstract:
Alzheimer's disease is characterized by a progressive neurodegenerative disorder, often misdiagnosed too late, with early symptoms that are hidden. Detection is crucial for effective treatment and slowing the progression of disease. We propose an upgraded version of the YOLO (You Only Look Once) framework, namely YOLOv8, for detecting Alzheimer's disease from MRI scans. Our approach seeks the detection of early structural changes in the brain, most particularly in the hippocampus and cortex, which are also among the first areas affected in this disease process. The framework performs state-of-the-art detection of Alzheimer's changes with a 96% precision via multi-scale feature extraction specifically designed for neuroimaging data. Results show this approach to be exceptionally effective in improving sensitivity and precision over existing techniques, marking it as a highly reliable method for early diagnosis of Alzheimer's disease.