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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.