Meta-analysis of Convolutional neural networks for radiological images – Pubrica

1. META-ANALYSIS OF CONVOLUTIONAL NEURAL NETWORKS FOR RADIOLOGICALIMAGES An Academic presentationby Dr.NancyAgens,Head,TechnicalOperations,Pubrica Group: www.pubrica.com Email:sales@pubrica.com

2. Today'sDiscussion Outline In-Brief Introduction Convolutional NeuralNetwork Architecture of CNN Applications in Radiology Advantages ofCNN FutureScopes Conclusion

3. 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.forMeta-analysis WritingServices.

4. Introduction The growth of massive datasets creates a need for more advanced tools for analysis. CNN is such a tool that is mainly foranalyzing theimages. Currently, in healthcare and clinical management, it is used for diabetic retinopathy screening, skin lesion classification, and lymph node metastasis detection for meta-analysisresearch. Contd..

5. 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,CNNandRadiologyfindamutualrelationshipinmeta-analysis paperwriting

6. Convolutional Neural Network(CNN) Convolution Neural Networkis also known asConvents. CNN is an in-depth learning approach that was inspired by the animal visualcortex. The design is to adapt and learn low to high- levelpatterns. Contd..

7. In this, there are specific terms used, each defining certain things– Parameter: A variable that is automatically learning process with the meta- analysisexperts Hyperparameter: A variable that needs to be performed beforetraining Kernel: A set of learnableparameters.

8. Architecture ofCNN Writinga meta-analysis paperabout thenetwork comprises three blocks – Convolution, pooling, connectedblocks. The initial two layers perform feature extraction,and the final one produces theoutput. A typical convolution layer contains a stack ofthese layers in a repeatedorder. Convolution layer is the fundamental layer of CNNthat consists of a combination of linear and nonlinear operations. Contd..

9. The main feature of convolution operation is weightsharing. The output of the convolution layer passes through the nonlinear activationfunction. Pooling layers reduce the dimensionality and combine the outputs of theprevious layers into a single neuron present in the nextlayer. Max pooling is the popular pooling operation which utilizes maximum neuronclusters. Contd..

10. Connected layers connect all neurons in aline. It works by abiding the principle of Multi-Layer Perceptron. Every fully connected layer follows a nonlinearfunction.

11. Applications inRadiology While analyzing the medical images,classification takes place by targeting the lesions andtumours. Other categories of those are into two or moreclasses. Many training data is there for better type usingCNN. After the classification process, thesegmentation process takesplace. Segmentation of organs is the crucial role inimage processingtechniques. Contd..

12. Segmentation is a time-consumingprocess. Instead of manual segmentation, CNN can be applied for segmenting theorgans. To train the network for the segmentation process, medical images of the organsand those segmentation results areused. CNN classifier is used for segmentation to calculate the probability of finding theorgans. In this, firstly, a probability map of the organs using CNN is done, later, global contextof images and other probability maps by conductingameta-analysis. Contd..

13. After all these, the abnormalities within themedical imagesmust bedetected. In previous studies, 2D-CNN is used for detecting TB on chestradiographs. For develop the detection system and evaluate its performance, the dataset of1007 chest radiographs performswell. About 40 million mammography examinations are done every year in theUSA. Those were made mainly to screen programs aiming to detect breast cancer atearly stages by the meta-analysis in quantitativestudies

14. Advantages ofCNN Currently, specific techniques like texture analysis, conventional machine learning classifiers like randomforests and support vector machines areuseful. Howbeit, CNN posses itsadvantages. It does not need hand-made featureextraction. Then, the architecture of CNNdoes notrequire segmentation of parts like differentiating tumors andorgans. Contd..

16. Future Scopes There are several methods to facilitate deeplearning. But, well-annotated medical datasets in huge size arerequired to accomplish the perspectives of deepunderstanding. This kind of dedicated pre-trained networks can be usedto foster the advancementofmedical diagnosis. The vulnerability of deep neural networks in medical imaging is crucial since the clinical application requires robustness for eventual applications compared to other non-medicalsystems.

17. Conclusion More datasets are produced in both medicaland non-medicalfields. It has become obvious to apply more deeplearning to ease analyzing and recognizingthem. CNN's and other deep learning techniques are helpful in healthcare and health riskmanagement guided by the help of Pubricaand giving Meta- analysis WritingServices.

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