THYROID NODULES SEGMENTATION USING DEEP LEARNING APPROACHES
DOI:
https://doi.org/10.61841/89zefj30Keywords:
ultrasound,segmentation,deep learning,NodulesAbstract
ultrasound is an clear procedure that interpret the internal structure of an organ.It is an unique method that provides most important,rapid and clear evaluation in everymeans.Due to the presence of an unwanted noise in an image it is an challenging task to segementing an image.So in this work we introduce an effective method to segment the image and findout the problem more clear.ultrasound image is the most common method that used nowdays because of its imaging technique and the visibility of the internal structure of an organ.The medical reports usually offer quantative analysis of data due to the changes in prior study.Henceforth it is important to give information about an image based upon its size and shape.Image segmentation is the most common tools that used in processing the medical image.Different algorithms that are used to segment the ultrasound image and for further classification.
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References
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automated thyroid nodules segmentation and estimate the volume from Computerized tomography
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particle swarm optimization algorithm.
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to classify the nodular lessions of thyroid. Experimental results showed the proposed method classifies the thyroid nodules correctly and efficiently
29.Singh1 and Mrs Alka Jindal focuss theGLCM texture feature method used for orderinge of images
and these structures are to trained the classifiers such as SVM, KNNand Bayesian
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inthe thyroid ultrasound image using a MATLAB. The imageundergoes the contrast enhancement to suppress
speckle.Theenhancement image is used for further processing ofsegmentation
32.This paper proposed deep learning approaches such as CNN.CNN can be obtained by using 12 layers.The
network used in CNN was well trained and CT image is taken and augumentation process is done
33.By using multi layer networks and machine learning algorithms the image is segmented by using multiple layers
of information that can be followed by pattern analysis of classification..
34.Multi model image technique was most commonly used to segment the medical image.The segmented image
uses image fusion architecture based on concepts to obtain the image more clear
35.The loss in the noisy image can be demonstrated by the neural network to produce a high quality image obtained
by using weighed loss function.
By conducting deep learning methods for medical imageProcessing in multi modal image analysis by an
algorithmic architecture such as cross modality and feature learning extraction based on CNN
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