6 research outputs found
A Review on the Applications of Machine Learning for Tinnitus Diagnosis Using EEG Signals
Tinnitus is a prevalent hearing disorder that can be caused by various
factors such as age, hearing loss, exposure to loud noises, ear infections or
tumors, certain medications, head or neck injuries, and psychological
conditions like anxiety and depression. While not every patient requires
medical attention, about 20% of sufferers seek clinical intervention. Early
diagnosis is crucial for effective treatment. New developments have been made
in tinnitus detection to aid in early detection of this illness. Over the past
few years, there has been a notable growth in the usage of
electroencephalography (EEG) to study variations in oscillatory brain activity
related to tinnitus. However, the results obtained from numerous studies vary
greatly, leading to conflicting conclusions. Currently, clinicians rely solely
on their expertise to identify individuals with tinnitus. Researchers in this
field have incorporated various data modalities and machine-learning techniques
to aid clinicians in identifying tinnitus characteristics and classifying
people with tinnitus. The purpose of writing this article is to review articles
that focus on using machine learning (ML) to identify or predict tinnitus
patients using EEG signals as input data. We have evaluated 11 articles
published between 2016 and 2023 using a systematic literature review (SLR)
method. This article arranges perfect summaries of all the research reviewed
and compares the significant aspects of each. Additionally, we performed
statistical analyses to gain a deeper comprehension of the most recent research
in this area. Almost all of the reviewed articles followed a five-step
procedure to achieve the goal of tinnitus. Disclosure. Finally, we discuss the
open affairs and challenges in this method of tinnitus recognition or
prediction and suggest future directions for research
Photonic Neural Networks: A Compact Review
It has long been known that photonic science and especially photonic
communications can raise the speed of technologies and producing manufacturing.
More recently, photonic science has also been interested in its capabilities to
implement low-precision linear operations, such as matrix multiplications, fast
and effciently. For a long time most scientists taught that Electronics is the
end of science but after many years and about 35 years ago had been understood
that electronics do not answer alone and should have a new science. Today we
face modern ways and instruments for doing tasks as soon as possible in
proportion to many decays before. The velocity of progress in science is very
fast. All our progress in science area is dependent on modern knowledge about
new methods. In this research, we want to review the concept of a photonic
neural network. For this research was selected 18 main articles were among the
main 30 articles on this subject from 2015 to the 2022 year. These articles
noticed three principles: 1- Experimental concepts, 2- Theoretical concepts,
and, finally 3- Mathematic concepts. We should be careful with this research
because mathematics has a very important and constructive role in our topics!
One of the topics that are very valid and also new, is simulation. We used to
work with simulation in some parts of this research. First, briefly, we start
by introducing photonics and neural networks. In the second we explain the
advantages and disadvantages of a combination of both in the science world and
industries and technologies about them. Also, we are talking about the
achievements of a thin modern science. Third, we try to introduce some
important and valid parameters in neural networks. In this manner, we use many
mathematic tools in some portions of this article
Alzheimers Disease Diagnosis by Deep Learning Using MRI-Based Approaches
The most frequent kind of dementia of the nervous system, Alzheimer's
disease, weakens several brain processes (such as memory) and eventually
results in death. The clinical study uses magnetic resonance imaging to
diagnose AD. Deep learning algorithms are capable of pattern recognition and
feature extraction from the inputted raw data. As early diagnosis and stage
detection are the most crucial elements in enhancing patient care and treatment
outcomes, deep learning algorithms for MRI images have recently allowed for
diagnosing a medical condition at the beginning stage and identifying
particular symptoms of Alzheimer's disease. As a result, we aimed to analyze
five specific studies focused on AD diagnosis using MRI-based deep learning
algorithms between 2021 and 2023 in this study. To completely illustrate the
differences between these techniques and comprehend how deep learning
algorithms function, we attempted to explore selected approaches in depth
Deep Learning Techniques for Cervical Cancer Diagnosis based on Pathology and Colposcopy Images
Cervical cancer is a prevalent disease affecting millions of women worldwide
every year. It requires significant attention, as early detection during the
precancerous stage provides an opportunity for a cure. The screening and
diagnosis of cervical cancer rely on cytology and colposcopy methods. Deep
learning, a promising technology in computer vision, has emerged as a potential
solution to improve the accuracy and efficiency of cervical cancer screening
compared to traditional clinical inspection methods that are prone to human
error. This review article discusses cervical cancer and its screening
processes, followed by the Deep Learning training process and the
classification, segmentation, and detection tasks for cervical cancer
diagnosis. Additionally, we explored the most common public datasets used in
both cytology and colposcopy and highlighted the popular and most utilized
architectures that researchers have applied to both cytology and colposcopy. We
reviewed 24 selected practical papers in this study and summarized them. This
article highlights the remarkable efficiency in enhancing the precision and
speed of cervical cancer analysis by Deep Learning, bringing us closer to early
diagnosis and saving lives
CRC-ICM: Colorectal Cancer Immune Cell Markers Pattern Dataset
Colorectal Cancer (CRC) is the second most common cause of cancer death in
the world, ad can be identified by the location of the primary tumor in the
large intestine: right and left colon, and rectum. Based on the location, CRC
shows differences in chromosomal and molecular characteristics, microbiomes
incidence, pathogenesis, and outcome. It has been shown that tumors on left and
right sides also have different immune landscape, so the prognosis may be
different based on the primary tumor locations. It is widely accepted that
immune components of the tumor microenvironment (TME) plays a critical role in
tumor development. One of the critical regulatory molecules in the TME is
immune checkpoints that as the gatekeepers of immune responses regulate the
infiltrated immune cell functions. Inhibitory immune checkpoints such as PD-1,
Tim3, and LAG3, as the main mechanism of immune suppression in TME
overexpressed and result in further development of the tumor. The images of
this dataset have been taken from colon tissues of patients with CRC, stained
with specific antibodies for CD3, CD8, CD45RO, PD-1, LAG3 and Tim3. The name of
this dataset is CRC-ICM and contains 1756 images related to 136 patients. The
initial version of CRC-ICM is published on Elsevier Mendeley dataset portal,
and the latest version is accessible via: https://databiox.co
ERCPMP: An Endoscopic Image and Video Dataset for Colorectal Polyps Morphology and Pathology
In the recent years, artificial intelligence (AI) and its leading subtypes,
machine learning (ML) and deep learning (DL) and their applications are
spreading very fast in various aspects such as medicine. Today the most
important challenge of developing accurate algorithms for medical prediction,
detection, diagnosis, treatment and prognosis is data. ERCPMP is an Endoscopic
Image and Video Dataset for Recognition of Colorectal Polyps Morphology and
Pathology. This dataset contains demographic, morphological and pathological
data, endoscopic images and videos of 191 patients with colorectal polyps.
Morphological data is included based on the latest international
gastroenterology classification references such as Paris, Pit and JNET
classification. Pathological data includes the diagnosis of the polyps
including Tubular, Villous, Tubulovillous, Hyperplastic, Serrated, Inflammatory
and Adenocarcinoma with Dysplasia Grade & Differentiation. The current version
of this dataset is published and available on Elsevier Mendeley Dataverse and
since it is under development, the latest version is accessible via:
https://databiox.com