Meta-Analysis of Convolutional Neural Networks for Radiological Images Dr.. In which, Convolutional Neural Network is a modern approach to visualize the images with high performance..
Trang 1Meta-Analysis of Convolutional Neural Networks for Radiological Images
Dr Nancy Agens, Head, Technical Operations, Pubrica sales@pubrica.com
In-Brief
Deep Learning is an inevitable branch of
Artificial Intelligence technology In
which, Convolutional Neural Network is
a modern approach to visualize the
images with high performance These
networks help for high performance in
the recognition and categorization of
images It has found applications in the
modern science sectors such as
Pharmaceuticals, etc for Meta-analysis
Writing Services.
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I INTRODUCTION
The growth of massive datasets
creates a need for more advanced tools for
analysis CNN is such a tool that is mainly
for analyzing the images Currently, in
healthcare and clinical management, it is
used for diabetic retinopathy screening,
skin lesion classification, and lymph node
metastasis detection for meta-analysis
research Radiology is a scientific front
used in the healthcare sector for
diagnosing various types of diseases via
different imaging techniques like ultrasound, X-ray radiography, MRI Therefore, CNN and Radiology find a mutual relationship in meta-analysis paper writing
II CONVOLUTIONAL NEURAL
NETWORK (CNN)
Convolution Neural Network is also known as Convents CNN is an in-depth learning approach that was inspired
by the animal visual cortex The design is
to adapt and learn low to high-level patterns In this, there are specific terms used, each defining certain things – (i) Parameter: A variable that is automatically learning process with the meta-analysis experts (ii) Hyperparameter: A variable that needs to be performed before training (iii) Kernel: A set of learnable parameters III ARCHITECTURE OF CNN
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about the network comprises three blocks – Convolution, pooling, connected blocks The initial two layers perform feature extraction, and the final one produces the output A typical convolution layer contains a stack of these layers in a repeated order
Convolution layer is the fundamental layer of CNN that consists of
a combination of linear and nonlinear operations The main feature of convolution operation is weight sharing The output of the convolution layer passes through the nonlinear activation function
Trang 2Pooling layers reduce the
dimensionality and combine the outputs of
the previous layers into a single neuron
present in the next layer Max pooling is
the popular pooling operation which
utilizes maximum neuron clusters
Connected layers connect all
neurons in a line It works by abiding the
principle of Multi-Layer Perceptron Every
fully connected layer follows a nonlinear
function
IV APPLICATIONS IN RADIOLOGY
While analyzing the medical
images, classification takes place by
targeting the lesions and tumours Other
categories of those are into two or more
classes Many training data is there for
better type using CNN
After the classification process, the
segmentation process takes place
Segmentation of organs is the crucial role
in image processing techniques
Segmentation is a time-consuming
process Instead of manual segmentation,
CNN can be applied for segmenting the
organs To train the network for the
segmentation process, medical images of
the organs and those segmentation results
are used
CNN classifier is used for
segmentation to calculate the probability
of finding the organs In this, firstly, a
probability map of the organs using CNN
is done, later, global context of images and
other probability maps by conducting a
meta-analysis
After all these, the abnormalities
within the medical images must be
detected Those abnormalities may be
existing or may not be in typical cases In
previous studies, 2D-CNN is used for
detecting TB on chest radiographs For
develop the detection system and evaluate
its performance, the dataset of 1007 chest
radiographs performs well
About 40 million mammography
examinations are done every year in the
USA Those were made mainly to screen
programs aiming to detect breast cancer at early stages by the meta-analysis in quantitative studies
V ADVANTAGES OF CNN
Currently, specific techniques like texture analysis, conventional machine learning classifiers like random forests and support vector machines are useful Howbeit, CNN posses its advantages It does not need hand-made feature extraction Then, the architecture of CNN
does not require segmentation of parts like differentiating tumors and organs
VI FUTURE SCOPES There are several methods to facilitate deep learning But, well-annotated medical datasets in huge size are required to accomplish the perspectives of deep understanding This kind of dedicated pre-trained networks can be used to foster the advancement of medical diagnosis The vulnerability of deep neural networks in medical imaging is crucial since the clinical application requires robustness for
Trang 3eventual applications compared to other
non-medical systems
VII CONCLUSION
More datasets are produced in both
medical and non-medical fields It has
become obvious to apply more deep
learning to ease analyzing and recognizing
them CNN's and other deep learning
techniques are helpful in healthcare and
health risk management guided by the help
of Pubrica and giving Meta-analysis
Writing Services
REFERENCES
1 Banerjee, I., Ling, Y., Chen, M C., Hasan, S A.,
Langlotz, C P., Moradzadeh, N., .&Farri, O
(2019) Comparative effectiveness of convolutional
neural network (CNN) and recurrent neural network
(RNN) architectures for radiology text report
classification Artificial intelligence in medicine, 97,
79-88
2 Lee, Y H (2018) Efficiency improvement in a
busy radiology practice: determination of
musculoskeletal magnetic resonance imaging
protocol using deep-learning convolutional neural
networks Journal of digital imaging, 31(5),
604-610
3 Yamashita, R., Nishio, M., Do, R K G., &Togashi,
K (2018) Convolutional neural networks: an
overview and application in radiology Insights into
Imaging, 9(4), 611-629