Intelligent Computational Models for Alzheimer's Disease Classification

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Medical image is a type of digital image processing that has widened its boundaries over the decades. It is a primary source for the visual representation of a human system's internal organs for clinical interpretation and intervention. The growth and developments in this field have triggered the development of novel algorithms for data/ image processing. The medical imaging modalities are medical imaging and processing, Structural Magnetic Resonance Imaging (sMRI), Functional Magnetic Resonance Imaging (fMRI), Digital Mammogram, Ultra Sound (US), Positron Emission Tomography (PET), Electrocardiogram (ECG), and Computer Tomography (CT) that have promoted research in many allied fields including Geographical Information Systems (GIS), Forensic Science, Business Data Analytics and Astronomy. Digital Image is mathematically represented as a twodimensional function, f(x, y) where x and y are the spatial coordinates, and the amplitude f at any given pair of coordinates (x, y) is called intensity. Digital Image Processing (DIP) is the subject area that deals with processing/ manipulating a digital image by a digital computer to obtain useful and necessary information from it. The different elements of a DIP system include image acquisition, image storage, and image processing. Medical image processing is an offshoot of DIP, which involves using technology to process the human body's medical images. Medical imaging is also referred to as diagnostic imaging. Medical imaging aims to visualize the human body's inner parts, which is required to identify internal abnormalities such as broken bones, tumors, and leaking blood vessels. In medical imaging, segmentation aims to study anatomical structure, demarcate and distinguish a region and help in therapeutic planning and treatment. Recent medical image diagnosis encompasses the advancement of the latest technology for acquiring the medical images by different modalities used for diagnosis, treatment, and research. Medical image processing covers a broad array of operations categorized as low-level, mid-level, and high-level image processing based on computation complexity. Medical image segmentation contributes to analyzing human anatomy and diagnosing the disorders, using automated or semi-automated computational systems. The foremost objective of medical image segmentation is to extract the regions to study the intrinsic anatomical structure and estimate the severity of abnormalities that help in deciding the means and methods of treatment. These aspects motivate researchers to develop competent computing techniques for pre-processing, segmentation, and analysis. These techniques' novelty is commonly ascertained by three metrics, namely, precision, recall, and F1-Score.