The document discusses the application of convolutional neural networks (CNNs) in radiology, highlighting their effectiveness in image analysis for healthcare tasks such as diabetic retinopathy screening and tumor classification. It explains the CNN architecture, which includes layers for convolution, pooling, and output, along with the benefits of using CNNs over traditional methods. Additionally, it emphasizes the need for large, well-annotated medical datasets to enhance deep learning applications in medical imaging.

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Meta-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
Healthcare, Bioinformatics,
Pharmaceuticals, etc. for Meta-analysis
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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
Writing a meta-analysis paper
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.

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Pooling 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

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eventual 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
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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.