China 
Africa 
Explore chapters and articles related to this topic
Lung Tumor Segmentation Using a 3D Densely Connected Convolutional Neural Network
Published in Mohan Lal Kolhe, Kailash J. Karande, Sampat G. Deshmukh, Artificial Intelligence, Internet of Things (IoT) and Smart Materials for Energy Applications, 2023
Shweta Tyagi, Sanjay N. Talbar
For diagnosis of a disease or an injury, there are several medical imaging techniques like X-rays, magnetic resonance imaging (MRI), computed tomography (CT) imaging, positron emission tomography (PET) imaging, sonography, mammography and so on. For patients having lung cancer, the diagnosis is usually done by using three medical imaging tests, X-ray images, CT scans and PET scans, out of which CT scan is preferred because this is more reliable as compared to the chest X-ray images in predicting the nature of the tumor, and it can provide more information about certain lung tumor features, including its size, shape and internal density. The CT scan is examined by the radiologists to detect the tumor region in the lungs. But this process is very time-consuming because one CT scan consists of hundreds of slices, and the number of lung cancer patients is also very high, due to which there is a huge burden on radiologists, especially in undeveloped or underdeveloped countries where there are not enough medical experts to examine the cancer imaging tests. To reduce this burden and to provide a second opinion to the doctors, several researchers have proposed different image processing and deep learning techniques for lung cancer detection and analysis. First step in automatic lung cancer detection is tumor segmentation, and if the tumor is segmented correctly then only it can be analyzed in a much better sense.
Basic Understanding of Medical Image Processing
Published in Sanjay Saxena, Sudip Paul, High-Performance Medical Image Processing, 2022
Pradeep Kumar, Subodh Srivastava, Y. Padma Sai
Medical imaging is the methodology through which visual representation of the interior of a body for clinical analysis and medical intervention is created, as well as visual representation of the function of some organs or tissues is generated.
Biomedical Engineering and Informatics Using Artificial Intelligence
Published in Saravanan Krishnan, Ramesh Kesavan, B. Surendiran, G. S. Mahalakshmi, Handbook of Artificial Intelligence in Biomedical Engineering, 2021
Medical imaging is a collection of techniques that are used to create visual representations of a body interior for clinical analysis and medical intervention. Medical imaging plays an important role in medical diagnosis, treatment, and medical applications, which seeks to reveal internal structures hidden by the skin and bones for diagnosing and treating disease. In medicine, AI is used to identify diagnosis and give therapy recommendations. In medical diagnosis, artificial neural networks (ANNs) is used to get the result of the diagnosis. ANN provides an extraordinary level of achievement in the medical field. ANN has been applied to various areas in medicine like disease diagnosis, biochemical analysis, image analysis, etc. In recent years, medical image processing uses ANNs for analyzing medical images. The main components of medical image processing that heavily depend on ANNs are medical image object detection and recognition, medical image segmentation, and medical image preprocessing. The various AI imaging technologies help to examine various factors of the human body using radiography, MRI, nuclear medicine, ultrasound imaging, tomography, cardiograph, and so on (Smita et al., 2012).
An efficient stacked ensemble model for the detection of COVID-19 and skin cancer using fused feature of transfer learning and handcrafted methods
Published in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2023
Over the years, many computer-aided systems have been developed to diagnose and detect human disease by using medical images. The most recent imaging modalities rely on high-resolution imaging to provide radiologists with multi-oriented representations, which helps them with a clinical diagnosis and perform accurate predictions and treatment for a patient. Ultrasound, magnetic resonance imaging (MRI), x-ray computed tomography and endoscopy are some of the most common modalities for medical imaging (Chowdhary and Acharjya 2020; Houssein et al. 2021c). Due to the fast development of medical imaging technologies, the field of digital pathology, which concentrates on the analysis and management of image information produced by these technologies, is expanding quickly (Mormont et al. 2018). Digital pathology with machine learning and deep learning technologies holds considerable potential for medical practices (diagnostic medicine and disease prediction) and research studies related to biomedical (Foucart et al. 2019; Fan et al. 2021; Houssein et al. 2022b).
An Approach for Diagnostically Lossless Coding of Volumetric Medical Data Based on Wavelet and Just-Noticeable-Distortion Model
Published in IETE Journal of Research, 2023
B. K. Chandrika, P. Aparna, S. Sumam David
Modern medical imaging modalities such as Computed Tomography (CT), Positron Emission Tomography (PET), Ultrasound, Magnetic Resonance Imaging (MRI), etc., have revolutionised the health care systems. Improved inter-slice distance, increased image resolution, and medical image data volume plays a significant role in early intervention and detailed diagnosis of health condition resulting in substantial image data. Hence, the paramount problem is to manage this enormous amount of data for storage and transmission. Medical diagnostic techniques for detection of abnormalities demand high visual quality in the image, which is possible with lossless compression. But these lossless compression techniques offer poor compression that can't meet the current compression requirements.
A scaling up approach: a research agenda for medical imaging analysis with applications in deep learning
Published in Journal of Experimental & Theoretical Artificial Intelligence, 2023
Yaw Afriyie, Benjamin A. Weyori, Alex A. Opoku
Often referred to as medical imaging, these techniques provide visual information about the human body. In medical imaging, radiologists and doctors are aimed at improving diagnosis and treatment efficiency. Diagnostic and therapeutic imaging include a variety of imaging methods for determining a patient’s health status (S. H. Yoo et al., 2020). Many imaging modalities are used nowadays, and their use is becoming more widespread. From 1996 to 2010, Smith-Bindman (Smith-Bindman, 2012) studied imaging in six large United States healthcare systems, resulting in 30.9% of all imaging tests. During the study period, CT, MRI, and PET utilisation increased by 7.8%, 10%, and 57%, respectively. Images used in digital healthcare include ultrasound (US), X-ray, computed tomography (CT), magnetic resonance angiography (MRA) and magnetic resonance imaging (MRI), retinal photography, histology slides, colonoscopy, and dermoscopy images. The study adopts some images from the works of Ker et al. (Ker & Wang, 2018) in Figure 1 that depict some medical images. While some of these technologies assess numerous organs (CT and MRI), others are organ-specific (retina photography, dermoscopy). There are also differences in the amount of data generated by the various studies. Histology slides can be small image files, but MRIs can be huge. As a result, data preprocessing and algorithm architecture must be designed differently under processor and memory constraints.