Recent trend in medical imaging modalities and their applications in disease diagnosis: a review

• Published in: Multimedia tools and applications Vol. 83; no. 14; pp. 43035 - 43070
• Main Authors: Abhisheka, Barsha, Biswas, Saroj Kumar, Purkayastha, Biswajit, Das, Dolly, Escargueil, Alexandre
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.04.2024; Springer Nature B.V
• Subjects: Computed tomography; Computer Communication Networks; Computer Science; Data Structures and Information Theory; Deep learning; Diagnosis; Disease; Health care; Health services; Image enhancement; Image processing; Literature reviews; Machine learning; Magnetic resonance imaging; Medical diagnosis; Medical electronics; Medical imaging; Multimedia Information Systems; Positron emission; Special Purpose and Application-Based Systems; Tomography; Track 2: Medical Applications of Multimedia; Medical Image Processing (MIP); Medical Imaging Informatics (MII); Medical Imaging (MI) Modalities; Disease Diagnosis Techniques; Deep Convolutional Neural Network (DCNN)
• ISSN: 1573-7721; 1380-7501; 1573-7721
• DOI: 10.1007/s11042-023-17326-1

Medical Imaging (MI) plays a crucial role in healthcare, including disease diagnosis, treatment, and continuous monitoring. The integration of non-invasive techniques such as X-ray, Positron Emission Tomography (PET) scan, Computed Tomography (CT) scan, Magnetic Resonance Imaging (MRI), and Ultrasound, has significantly enhanced medical treatment. MI enables visualization of internal structures without invasive procedures, aiding in the diagnosis of various diseases. The introduction of Medical Image Processing (MIP) has further improved disease prediction, detection, analysis, and evaluation. MIP data is utilized in Machine Learning (ML) and Deep Learning (DL) models to develop intelligent systems that enhance medical assistance and better recognition, because human interpretation of medical images is error prone and exhaustive. However, accuracy is crucial for the provision of high-quality healthcare. This has motivated various works on MI using MIP therefore, this paper emphasizes how these imaging modalities can be used to analyze, model, and manipulate data in order to achieve maximum treatment outcome. Moreover, a comprehensive literature survey is conducted to provide a detailed analysis of the working principles, benefits, and limitations of diverse imaging modalities. It explores state-of-the-art methodologies rooted in MI approaches and highlights potential future developments, challenges, trends, observations, and significant improvements in the field.