Bleaching,
dying,
straightening,
curling,
and
other
chemical
treatments
for
hair
are
becoming
increasingly
common
around
the
world
as
people's
interest
in
hairstyles
colouring
is
increasing.
As
a
result,
has
sustained
significant
damage
that
can
be
observed
with
naked
eye
by
touching
texture.
The
chemicals
applied
to
produce
severe
health
issues
such
skin
cancer,
migraine,
fall.
Despite
dangerous
consequences
of
treatments,
people
still
applying
these
chemicals.
disease
detected
at
its
early
stages
lead
reducing
loss
avoiding
cancer
migraine.
With
advancements
technologies,
methods
detection
also
developing.
In
proposed
work,
dataset
been
collected
from
Kaggle
which
further
implemented
using
convolutional
neural
network
model.
results
have
calculated
different
epochs
two
optimizers
namely,
SGD
Adam
identified
model
outperforms
epoch
85
ADAM
optimizer
achieving
an
accuracy
rate
95%.
achieved
highest
89%
50.
This
better
outcomes
when
compared
existing
models.
In
the
field
of
dermatology,
skin
disorders,
particularly
hair-related
conditions,
present
a
significant
challenge.
Image-based
automated
categorization
hair
problems
has
gained
research
attention
due
to
its
potential
assist
dermatologists
in
process
early
diagnosis
and
treatment
planning.
Transfer
learning,
technique
that
utilizes
pre-trained
deep
neural
networks,
proven
be
valuable
various
computer
vision
applications.
This
study
investigates
application
transfer
learning
for
leveraging
multiclass
classification
disorders
by
utilizing
three
commonly
used
Convolutional
Neural
Network
(CNN)
architectures:
AlexNet,
VGG16,
ResNet50.
begins
with
collection
comprehensive
dataset
comprising
high-resolution
images
including
but
not
limited
alopecia
areata,
tinea
capitis,
androgenetic
alopecia.
Categorizing
into
different
groups
based
on
type
severity
each
condition
enables
thorough
evaluation
models.
approach
is
employed
fine-tuning
these
network
architectures
using
disease
dataset.
A
hyperparameter
tuning
strategy
also
adopted
optimize
parameters
such
as
rates,
batch
sizes,
optimization
methods
enhance
model
performance.
The
results
reveal
all
architectures,
ResNet50,
achieve
99%
accuracy
rate
classifying
diseases.
Such
technology
their
clinical
practice
enabling
rapid
precise
detection,
thereby
improving
patient
outcomes
healthcare
efficiency.
Further
could
explore
integration
models
workflows
telemedicine
remote
consultation.
Electronics,
Год журнала:
2023,
Номер
12(6), С. 1380 - 1380
Опубликована: Март 14, 2023
The
World
Health
Organization
and
Korea
National
Insurance
assert
that
the
number
of
alopecia
patients
is
increasing
every
year,
approximately
70
percent
adults
suffer
from
scalp
problems.
Although
a
genetic
problem,
it
difficult
to
diagnose
at
an
early
stage.
deep-learning-based
approaches
have
been
effective
for
medical
image
analyses,
challenging
generate
deep
learning
models
detection
analysis
because
creating
dataset
challenging.
In
this
paper,
we
present
approach
generating
model
specialized
achieves
high
accuracy
by
applying
data
preprocessing,
augmentation,
ensemble
analyses.
We
use
containing
526
good,
13,156
mild,
3742
moderate,
825
severe
images.
was
further
augmented
normalization,
geometry-based
augmentation
(rotate,
vertical
flip,
horizontal
crop,
affine
transformation),
PCA
augmentation.
compare
performance
single
using
ResNet,
ResNeXt,
DenseNet,
XceptionNet,
ensembles
these
models.
best
result
achieved
when
ResNet
were
combined
achieve
95.75
F1
score
87.05.
Skin Research and Technology,
Год журнала:
2024,
Номер
30(4)
Опубликована: Март 28, 2024
Hair
and
scalp
disorders
present
a
significant
challenge
in
dermatology
due
to
their
clinical
diversity
overlapping
symptoms,
often
leading
misdiagnoses.
Traditional
diagnostic
methods
rely
heavily
on
expertise
are
limited
by
subjectivity
accessibility,
necessitating
more
advanced
accessible
tools.
Artificial
intelligence
(AI)
deep
learning
offer
promising
solution
for
accurate
efficient
diagnosis.
Healthcare,
Год журнала:
2025,
Номер
13(4), С. 395 - 395
Опубликована: Фев. 12, 2025
Background/Objectives:
Hair
loss
(alopecia
or
effluvium)
can
significantly
affect
the
self-esteem
and
psychosocial
well-being
of
patients,
resulting
in
a
reduced
quality
life.
It
may
herald
systemic
disease,
nutritional
deficiency,
side
effects
pharmacotherapy.
Current
therapeutic
options
for
hair
are
not
always
satisfactory
be
associated
with
considerable
effects;
therefore,
new
solutions
still
sought.
Caffeine
seems
to
an
effective
agent
against
thanks
its
stimulating
on
cell
growth
good
penetration
into
follicle.
The
aim
this
study
was
systematically
review
published
clinical
trials
topical
caffeine
preparations
loss.
Methods:
We
searched
PubMed,
Scopus,
Web
Science
investigating
efficacy
products
loss,
until
29
November
2024.
evidence
assessed
using
GRADE
classification.
Results:
query
returned
1121
articles,
which
9
ultimately
met
inclusion
criteria.
In
total,
684
people
androgenetic
alopecia,
excessive
thinning
were
included
these
trials.
all
studies,
conclusions
favor
treatment;
however,
level
scientific
medium
3
low
1,
very
remaining
5.
Their
major
flaws
lack
randomization
placebo
control
groups,
as
well
information
concentration
products.
Conclusions:
Results
from
studies
date
suggest
that
safe
Nevertheless,
better-designed
well-defined
required
ultimate
statement.
Commercial
offered
market
nowadays
worth
try,
but
due
incomplete
data
product
information,
outcomes
guaranteed.
EUREKA Health Sciences,
Год журнала:
2023,
Номер
3, С. 28 - 45
Опубликована: Авг. 7, 2023
The
aim:
study
aimed
to
determine
the
prevalence
of
several
non-communicable
diseases
(NCD)
and
analyze
risk
factors
among
adult
patients
seeking
nutritional
guidance
in
Dhaka,
Bangladesh.
Participants:
146
hospitalized
adults
both
genders
aged
18-93
participated
this
cross-sectional
research.
Methods:
We
collected
demographic
vital
information
from
Bangladesh.
checked
physical
parameters,
including
blood
sugar,
serum
creatinine,
pressure,
presence
or
absence
major
diseases.
Then
we
used
descriptive
statistical
approaches
explore
NCDs
based
on
gender
age
group.
Afterwards,
relationship
between
different
NCD
pairs
with
their
combined
effects
was
analyzed
using
hypothesis
testing
at
a
95
%
confidence
level.
Finally,
random
forest
XGBoost
machine
learning
algorithms
are
predict
comorbidity
underlying
responsible
factors.
Result:
Our
observed
relationships
gender,
groups,
obesity,
(DM,
CKD,
IBS,
CVD,
CRD,
thyroid).
most
frequently
reported
cardiovascular
issues
(CVD),
which
present
83.56
all
participants.
CVD
more
common
male
Consequently,
participants
had
higher
pressure
distribution
than
females.
Diabetes
mellitus
(DM),
other
hand,
did
not
have
gender-based
inclination.
Both
DM
an
age-based
progression.
showed
that
chronic
respiratory
illness
frequent
middle-aged
younger
elderly
individuals.
Based
data,
every
one
five
obese.
comorbidities
found
31.5
population
has
only
NCD,
30.1
two
NCDs,
38.3
NCDs.
Besides,
86.25
diabetic
issues.
All
thyroid
our
CVD.
Using
t-test,
CKD
(p-value
0.061).
Males
under
35
years
statistically
significant
0.018).
also
association
over
65
0.038).
Moreover,
there
been
Thyroid
(P<0.05)
for
those
below
35-65.
two-way
ANOVA
test
find
interaction
heart
combination
diabetes.
RTI
affected
old.
Among
algorithms,
produced
highest
accuracy,
69.7
%,
detection.
Random
feature
importance
detected
age,
weight
waist-hip
ratio
as
behind
comorbidity.
Conclusion:
helps
identify
future
risks
vulnerable
groups.
By
initiating
implementing
control
plans
study,
it
is
possible
reduce
burden
Hair
losses
diseases
are
common
and
pose
challenges
in
diagnosing
accurately
promptly.
Traditional
diagnostic
methods
involve
visual
medical
tests
by
dermatologists,
leading
to
delays
that
worsen
conditions.
To
address
this,
a
deep
learning
solution
using
2D
convolutional
neural
network
(CNN)
was
implemented,
effectively
predicting
hair
loss
categories:
alopecia,
psoriasis,
folliculitis.
Challenges
included
limited
dataset
access
diverse
online
images
affecting
model
precision.
Besides
the
model's
success,
created
factors
like
SSB,
Ageing,
hierarchical
characteristics,
Stress,
valuable
for
future
research.
This
study's
significance
lies
aiding
timely
identification
via
applications,
benefiting
both
professionals
individuals.
The
project's
approach
hinges
on
leveraging
power
of
models
discern
intricate
patterns
within
frontal
facial
images.
utilization
Convolutional
Neural
Networks
(CNNs)
will
enable
automatic
extraction
relevant
information
from
images,
hence
enabling
capture
minute
variations
density,
coverage,
distribution.
These
learned
features
subsequently
serve
as
foundation
an
accurate
comprehensive
classification
system
aligns
with
Hamilton-Norwood
scale's
progressive
stages
loss.
sum
up,
goal
this
research
study
is
show
techniques
can
automatically
identify
different
photos.
Through
integration
networks
image
processing
skills,
aims
further
development
diagnosis
treatment
approaches,
ultimately
improving
lives
those
who
suffer
illness.
Alopecia
areata
is
an
autoimmune
disorder
resulting
in
rapid
and
unpredictable
hair
loss
on
the
scalp
or
body
as
immune
system
mistakenly
attacks
human
follicles.
In
United
States
alone,
about
6.7
million
people
experience
a
form
of
Alopecia.
Early
identification
condition
has
shown
notable
potential
improving
treatment
outcomes
reducing
complications.
To
diagnose
Alopecia,
researchers
have
proposed
use
deep
learning
(DL)
techniques
to
classify
images
healthy
alopecia-affected,
which
high
potential.
However,
research
implementing
relevant
DL
algorithms
field
detection
estimation
limited.
This
paper
presents
comparative
analysis
our
two
newly
optimized
Convolutional
neural
networks
(CNN)
with
other
existing
models.
For
training,
we
considered
datasets
comprised
alopecia-affected
hair.
Due
data
unavailability,
gathered
from
distinct
datasets:
one
Figaro1k
independently
created
dataset.
After
training
algorithms,
performed
contrastive
assessment
determine
most
effective
based
criteria.
We
hypothesized
that
initial
performance
base
network
would
be
closely
connected
subsequent
accuracy
algorithm
when
it
for
new
task.
As
expected,
modified
Inception-Resnet-v2
model
achieved
greatest
performance,
validation
97.94%
10.4%,
respectively.
The
experimental
results
indicated
serves
framework
Areata
classification.
Alopecia,
also
known
as
hair
loss,
is
a
term
used
to
describe
loss
from
the
scalp
or
other
parts
of
body.
It
can
be
caused
by
number
factors,
such
genetics,
hormonal
fluctuations,
illness
external
stressors.
The
five
typical
types
are
male
pattern
baldness,
which
typically
hereditary
and
hormonal,
characterized
receding
hairline
thinning
crown,
then
female
usually
causes
general
without
noticeable
hairline,
especially
crown.
Genetic
factors
involved.
Next
comes
Alopecia
Areata,
results
in
round
bald
spots
sudden
patchy
that
immune
system
has
targeted
follicle.
Then
there
Telogen
Effluvium,
denotes
temporary
illness,
stress
resulting
seam
area.
Finally,
Traction
gradually
fall
out
tight
combs
constant
pulling.
This
study
aims
utilize
AI-related
methods
predict
these
above
mentioned
patterns.
Journal of Intelligent & Fuzzy Systems,
Год журнала:
2023,
Номер
45(6), С. 11369 - 11380
Опубликована: Окт. 3, 2023
Alopecia
Areata
(AA)
is
one
of
the
most
widespread
diseases,
which
generally
classified
and
diagnosed
by
Computer
Aided
Diagnosis
(CAD)
models.
Though
it
improves
AA
diagnosis,
has
limited
interoperability
needs
skilled
radiologists
in
medical
image
interpretation.
This
problem
can
be
solved
developing
Deep
Learning
(DL)
models
with
CAD
for
accurately
diagnosing
patients.
Many
studies
engaged
only
specific
DL
such
as
Convolutional
Neural
Network
(CNN)
imaging,
provides
different
independent
results
many
parameters,
limits
their
generalizability
datasets.
To
combat
this
limitation,
work
proposes
an
Ensemble
Pre-Learned
Optimized
Long
Short-Term
Memory
(EPL-OLSTM)
model
classification.
Initially,
healthy
scalp
hair
images
are
separately
fed
to
pre-learned
CNN
structures,
i.e.
AlexNet,
ResNet,
InceptionNet
extract
deep
features.
Then,
these
features
passed
OLSTM,
Battle
Royale
Optimization
(BRO)
algorithm
applied
optimize
LSTM’s
hyperparameters.
Moreover,
output
LSTM
fuzzy-softmax
into
associated
classes,
including
mild,
moderate,
severe.
Thus,
increase
accuracy
differentiating
between
multiple
classes.
Finally,
extensive
experiment
using
Figaro1k
(for
images)
DermNet
datasets
demonstrates
that
EPL-OLSTM
achieves
93.1%
compared
state-of-the-art