Enhanced Classification of Alzheimer’s Disease Stages via Weighted Optimized Deep Neural Networks and MRI Image Analysis Mudiyala Aparna , Battula Srinivasa Rao * School of Computer Science and Engineering, VIT-AP University, Andhra Pradesh 522237, India Corresponding Author Email: srinivas.battula@vitap.ac.in https://doi.org/10.18280/ts.400538 ABSTRACT Received: 2 January 2023 Revised: 28 March 2023 Accepted: 16 May 2023 Available online: 30 October 2023 Alzheimer's disease, a debilitating neurological disorder, precipitates irreversible cognitive decline and memory loss, predominantly affecting individuals aged 65 years and above. The need for an automated system capable of accurately diagnosing and stratifying Alzheimer's disease into distinct stages is paramount for early intervention and management. However, existing deep learning methodologies are often hampered by protracted training times. In this study, a time-efficient approach incorporating a two-phase transfer learning technique is proposed to surmount this challenge. This method is particularly efficacious in the analysis of Magnetic Resonance Imaging (MRI) data for the identification of Alzheimer's disease. The proposed detection system employs two-phase transfer learning, augmented with fine-tuning for multi-class classification of brain MRI scans. This allows for the categorization of images into four distinct classes: Mild Dementia (MD), Moderate Dementia (MOD), Non-Dementia (ND), and Very Mild Dementia (VMD). The classification of Alzheimer's disease was conducted using various pre-trained deep learning models, including ResNet50V2, InceptionResNetV2, Xception, DenseNet121, VGG16, and MobileNetV2. Among the models tested, ResNet50V2 demonstrated superior performance, achieving a training classification accuracy of 99.35% and a testing accuracy of 99.25%. The results underscore the potential of the proposed method in delivering more accurate classifications than those obtained from extant models, thereby contributing to the early detection and stratification of Alzheimer's disease. Keywords: image processing techniques, Alzheimer’s data, optimization techniques, deep learning models, transfer learning techniques 1. INTRODUCTION Alzheimer's disease, a neurological disorder characterized by a gradual deterioration of memory, cognition, and basic task performance ability, predominantly afflicts individuals aged 65 and above. As the most prevalent cause of dementia in the nation, it currently ranks as the seventh leading cause of death [1]. The deterioration of brain tissues, culminating in neuronal death, precipitates memory loss and adversely impacts daily task performance, including reading, speaking, and writing. However, early diagnosis and intervention can enhance patients' quality of life [2-5]. The onset of symptoms is typically insidious, gradually exacerbating the patient's health condition over time. Predictive models project that by the year 2050, one in 85 individuals will be diagnosed with Alzheimer's disease, signifying a substantial annual case increase [6-8]. Approximately 60–80% of diagnosed cases progress to advanced stages of the disease. The Global Deterioration Scale (GDS) is frequently employed for dementia assessment, while the Clinical Dementia Rating (CDR) scale aids in understanding and communication with dementia patients [9-11]. Characteristic brain changes in Alzheimer's disease include enlarged ventricles and a reduction in the size of the cerebral cortex and hippocampus. The latter, when reduced, impairs both spatial and episodic memory. The neuronal damage that results contributes to difficulties in planning, judgement, and short- term memory. The ongoing cell degeneration further impairs synapses and neuronal terminals. Numerous investigations have focused on the categorization and early detection of Alzheimer's disease. Brain Magnetic Resonance Imaging (MRI) analysis is a common and effective method for disease identification. These MRI images are reviewed by medical professionals to detect the presence of abnormalities such as tumors, tissue changes, or degenerative conditions. The integration of deep learning and machine learning models with various medical imaging modalities, including mammography, ultrasound, and MRI, has been explored [12, 13]. These models have demonstrated significant results in disease classification and detection across various domains, including cardiovascular, pulmonary, neural, retinal, mammary, and skeletal diseases. In the present study, the utility of transfer learning is demonstrated in achieving accurate Alzheimer's disease diagnosis using two pre-trained base models. Existing diagnostic tests in neurology clinics are swift, cost-effective, and can identify Alzheimer's disease with accuracy exceeding 95%. However, comprehensive testing in most hospitals and clinics only achieves a 70% accuracy rate. This study's focus is on the nucleus accumbens, an integral brain region involved in motivation processing. This region within the ventral striatum is often overlooked in Alzheimer's research, primarily examined in studies focusing on emotional and motivational processes. A deep learning network was employed in this study to classify and identify Alzheimer's Traitement du Signal Vol. 40, No. 5, October, 2023, pp. 2215-2223 Journal homepage: http://iieta.org/journals/ts 2215