12 research outputs found
Invariant Scattering Transform for Medical Imaging
Invariant scattering transform introduces new area of research that merges
the signal processing with deep learning for computer vision. Nowadays, Deep
Learning algorithms are able to solve a variety of problems in medical sector.
Medical images are used to detect diseases brain cancer or tumor, Alzheimer's
disease, breast cancer, Parkinson's disease and many others. During pandemic
back in 2020, machine learning and deep learning has played a critical role to
detect COVID-19 which included mutation analysis, prediction, diagnosis and
decision making. Medical images like X-ray, MRI known as magnetic resonance
imaging, CT scans are used for detecting diseases. There is another method in
deep learning for medical imaging which is scattering transform. It builds
useful signal representation for image classification. It is a wavelet
technique; which is impactful for medical image classification problems. This
research article discusses scattering transform as the efficient system for
medical image analysis where it's figured by scattering the signal information
implemented in a deep convolutional network. A step by step case study is
manifested at this research work.Comment: 11 pages, 8 figures and 1 tabl
Case Studies on X-Ray Imaging, MRI and Nuclear Imaging
The field of medical imaging is an essential aspect of the medical sciences,
involving various forms of radiation to capture images of the internal tissues
and organs of the body. These images provide vital information for clinical
diagnosis, and in this chapter, we will explore the use of X-ray, MRI, and
nuclear imaging in detecting severe illnesses. However, manual evaluation and
storage of these images can be a challenging and time-consuming process. To
address this issue, artificial intelligence (AI)-based techniques, particularly
deep learning (DL), have become increasingly popular for systematic feature
extraction and classification from imaging modalities, thereby aiding doctors
in making rapid and accurate diagnoses. In this review study, we will focus on
how AI-based approaches, particularly the use of Convolutional Neural Networks
(CNN), can assist in disease detection through medical imaging technology. CNN
is a commonly used approach for image analysis due to its ability to extract
features from raw input images, and as such, will be the primary area of
discussion in this study. Therefore, we have considered CNN as our discussion
area in this study to diagnose ailments using medical imaging technology.Comment: 14 pages, 3 figures, 4 tables; Acceptance of the chapter for the
Springer book "Data-driven approaches to medical imaging
Active Learning on Medical Image
The development of medical science greatly depends on the increased
utilization of machine learning algorithms. By incorporating machine learning,
the medical imaging field can significantly improve in terms of the speed and
accuracy of the diagnostic process. Computed tomography (CT), magnetic
resonance imaging (MRI), X-ray imaging, ultrasound imaging, and positron
emission tomography (PET) are the most commonly used types of imaging data in
the diagnosis process, and machine learning can aid in detecting diseases at an
early stage. However, training machine learning models with limited annotated
medical image data poses a challenge. The majority of medical image datasets
have limited data, which can impede the pattern-learning process of
machine-learning algorithms. Additionally, the lack of labeled data is another
critical issue for machine learning. In this context, active learning
techniques can be employed to address the challenge of limited annotated
medical image data. Active learning involves iteratively selecting the most
informative samples from a large pool of unlabeled data for annotation by
experts. By actively selecting the most relevant and informative samples,
active learning reduces the reliance on large amounts of labeled data and
maximizes the model's learning capacity with minimal human labeling effort. By
incorporating active learning into the training process, medical imaging
machine learning models can make more efficient use of the available labeled
data, improving their accuracy and performance. This approach allows medical
professionals to focus their efforts on annotating the most critical cases,
while the machine learning model actively learns from these annotated samples
to improve its diagnostic capabilities.Comment: 12 pages, 8 figures; Acceptance of the chapter for the Springer book
"Data-driven approaches to medical imaging
The Prominence of Artificial Intelligence in COVID-19
In December 2019, a novel virus called COVID-19 had caused an enormous number
of causalities to date. The battle with the novel Coronavirus is baffling and
horrifying after the Spanish Flu 2019. While the front-line doctors and medical
researchers have made significant progress in controlling the spread of the
highly contiguous virus, technology has also proved its significance in the
battle. Moreover, Artificial Intelligence has been adopted in many medical
applications to diagnose many diseases, even baffling experienced doctors.
Therefore, this survey paper explores the methodologies proposed that can aid
doctors and researchers in early and inexpensive methods of diagnosis of the
disease. Most developing countries have difficulties carrying out tests using
the conventional manner, but a significant way can be adopted with Machine and
Deep Learning. On the other hand, the access to different types of medical
images has motivated the researchers. As a result, a mammoth number of
techniques are proposed. This paper first details the background knowledge of
the conventional methods in the Artificial Intelligence domain. Following that,
we gather the commonly used datasets and their use cases to date. In addition,
we also show the percentage of researchers adopting Machine Learning over Deep
Learning. Thus we provide a thorough analysis of this scenario. Lastly, in the
research challenges, we elaborate on the problems faced in COVID-19 research,
and we address the issues with our understanding to build a bright and healthy
environment.Comment: 63 pages, 3 tables, 17 figure
Introduction to Medical Imaging Informatics
Medical imaging informatics is a rapidly growing field that combines the
principles of medical imaging and informatics to improve the acquisition,
management, and interpretation of medical images. This chapter introduces the
basic concepts of medical imaging informatics, including image processing,
feature engineering, and machine learning. It also discusses the recent
advancements in computer vision and deep learning technologies and how they are
used to develop new quantitative image markers and prediction models for
disease detection, diagnosis, and prognosis prediction. By covering the basic
knowledge of medical imaging informatics, this chapter provides a foundation
for understanding the role of informatics in medicine and its potential impact
on patient care.Comment: 17 pages, 11 figures, 2 tables; Acceptance of the chapter for the
Springer book "Data-driven approaches to medical imaging
Generative Adversarial Networks for Data Augmentation
One way to expand the available dataset for training AI models in the medical
field is through the use of Generative Adversarial Networks (GANs) for data
augmentation. GANs work by employing a generator network to create new data
samples that are then assessed by a discriminator network to determine their
similarity to real samples. The discriminator network is taught to
differentiate between actual and synthetic samples, while the generator system
is trained to generate data that closely resemble real ones. The process is
repeated until the generator network can produce synthetic data that is
indistinguishable from genuine data. GANs have been utilized in medical image
analysis for various tasks, including data augmentation, image creation, and
domain adaptation. They can generate synthetic samples that can be used to
increase the available dataset, especially in cases where obtaining large
amounts of genuine data is difficult or unethical. However, it is essential to
note that the use of GANs in medical imaging is still an active area of
research to ensure that the produced images are of high quality and suitable
for use in clinical settings.Comment: 13 pages, 6 figures, 1 table; Acceptance of the chapter for the
Springer book "Data-driven approaches to medical imaging
AutoML Systems For Medical Imaging
The integration of machine learning in medical image analysis can greatly
enhance the quality of healthcare provided by physicians. The combination of
human expertise and computerized systems can result in improved diagnostic
accuracy. An automated machine learning approach simplifies the creation of
custom image recognition models by utilizing neural architecture search and
transfer learning techniques. Medical imaging techniques are used to
non-invasively create images of internal organs and body parts for diagnostic
and procedural purposes. This article aims to highlight the potential
applications, strategies, and techniques of AutoML in medical imaging through
theoretical and empirical evidence.Comment: 11 pages, 4 figures; Acceptance of the chapter for the Springer book
"Data-driven approaches to medical imaging
Introduction of Medical Imaging Modalities
The diagnosis and treatment of various diseases had been expedited with the
help of medical imaging. Different medical imaging modalities, including X-ray,
Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Nuclear Imaging,
Ultrasound, Electrical Impedance Tomography (EIT), and Emerging Technologies
for in vivo imaging modalities is presented in this chapter, in addition to
these modalities, some advanced techniques such as contrast-enhanced MRI, MR
approaches for osteoarthritis, Cardiovascular Imaging, and Medical Imaging data
mining and search. Despite its important role and potential effectiveness as a
diagnostic tool, reading and interpreting medical images by radiologists is
often tedious and difficult due to the large heterogeneity of diseases and the
limitation of image quality or resolution. Besides the introduction and
discussion of the basic principles, typical clinical applications, advantages,
and limitations of each modality used in current clinical practice, this
chapter also highlights the importance of emerging technologies in medical
imaging and the role of data mining and search aiming to support translational
clinical research, improve patient care, and increase the efficiency of the
healthcare system.Comment: 19 pages, 7 figures, 1 table; Acceptance of the chapter for the
Springer book "Data-driven approaches to medical imaging
Use of Wireless Sensor and Microcontroller to Develop Water-level Monitoring System
This paper presents the design and development process of Wireless Data Acquisition System (WiDAS) which is a multi-sensor system for water level monitoring. It also consists of a microcontroller (ATMega8L), a data display device and an ultrasonic distance sensor (Parallax Ping). This wireless based acquisition system can communicate through RF module (Tx-Rx) from the measurement sources, such as sensors and devices as digital or analog values over a period of time. The developed system has the option to store the data in the computer memory. It was tested in real time and showed continuous and correct data. The developed system is consisting of a number of features, such as low energy consumption, easy to operate and well-built invulnerability, which cangive successful results to measure the water level. Finally, its flexibility facilitates an extensive application span for self-directed data collection with trustworthy transmission in few sparse points over huge areas
Automatic Car Parking and Controlling System Using Programmable Logic Controller (PLC)
Well-organized vehicle parking can assist drivers gratifying by protecting car energy as well as time consuming. In this paper, the automation process of an automatic car parking system is designed using a fully functional ladder logic based LOGO!12/24 RC, which is a small programmable logic controller (PLC). Infrared sensor (IR) electronic sensors were installed at the entrance and departure gates to sense the car those are waiting for either entry or exit. After that it gives the input signals to PLC to count the number of vehicles entering and leaving the park respectively. The developed system automatically can monitor and restrict the vehicles inside the parking space. The number of cars available in the park will be the difference of the number of vehicles entering and the number of vehicles leaving. When a car approaches to the entry gate, PLC will decide whether any space is available or not. If no space is available, the PLC will then send signal to entry gate to keep the gate closed and turn on the indication “Car Park Full”. If there is space in the park, the entry gate will open to allow the car to enter the park. Similarly, at the time of exit, the PLC will send signal to the exit gate to open and allow the car to leave the park after paid the parking payment. All these activities make the car parking system completely automatic. The development of this system is cost effective and useful to make solutions to car parking space problems in city areas