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Method of intelligent analysis for liver tumour

An analysis method and technology for liver tumors, which is especially applied in the field of diagnosis involving an analysis method using ultrasonic detection combined with machine learning algorithms, which can solve the problems of lengthy inspection schedules and high medical costs

Pending Publication Date: 2021-06-08
粘曉菁 +1
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Problems solved by technology

[0006] Although ultrasound examination has its convenience, it is also limited. For example, factors such as operator experience, patient obesity, liver fibrosis or cirrhosis will affect the accuracy of ultrasound. Therefore, when ultrasound examination When a malignant tumor is suspected, a second imaging examination, such as CT or MRI, is usually arranged to assist the diagnosis. In addition to the high medical costs and lengthy inspection schedule of these two examinations, CT examination has more radiation exposure considerations

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  • Method of intelligent analysis for liver tumour
  • Method of intelligent analysis for liver tumour

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Embodiment Construction

[0035] see figure 1 and figure 2 Shown are respectively a schematic flow chart of the present invention and a schematic block diagram of the present invention. As shown in the figure: the present invention is a smart analysis method for liver tumors, which includes the following steps:

[0036] Step 1 s1 : Scanning an area corresponding to the liver outside a subject by emitting ultrasound, and obtaining an ultrasound image of a target liver tumor of the subject.

[0037] Step 2 s2: Input several reference ultrasound images of liver tumors, which include ultrasound images of benign and malignant liver tumors.

[0038] Step 3 s3: Classify the existing liver tumors according to the light and shade, shadow area and shape in the ultrasound images of the existing liver tumors, and accordingly classify the several reference ultrasound images of the liver tumors input in the above step 2 with Ultrasonic images of liver tumors in the tumor image point area are used to mark and ide...

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Abstract

A method of Intelligent Analysis is provided for liver tumour. It is a method using scanning acoustic tomography (SAT) with a deep learning algorithm for determining the risk of malignance for liver tumour. The method uses the abundant experiences of abdominal ultrasound specialists as a base to mark pixel areas of liver tumours in ultrasound images. The parameters and coefficients of empirical data are trained with the deep learning algorithm to establish a categoriser model reaching an accuracy rate up to 86 percent. Thus, with an SAT image, a help to doctor or ultrasound technician is obtained to determine the risk of malignance for liver tumour through the method, and to further provide a reference base for diagnosing liver tumour type.

Description

technical field [0001] The present invention relates to a smart analysis method for liver tumors, in particular to an analysis method that combines ultrasonic detection with machine learning algorithms to determine the risk of benign and malignant liver tumors, especially refers to scanning images that can be detected through ultrasonic waves. The tumor wisdom analysis method immediately assists doctors or ultrasound technicians to interpret the benign and malignant risks of liver tumors, thereby providing a reference for doctors to diagnose liver tumor types. Background technique [0002] Liver cancer is the fourth leading cause of cancer death in the world and the second among the top ten causes of cancer death among residents. The common causes of liver cancer are mostly caused by hepatitis B and C viruses and aflatoxins in Asia, and hepatitis C viruses are common in European and American countries, and the phenomenon of liver cancer caused by steatohepatitis, diabetes an...

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Application Information

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IPC IPC(8): G06T7/00G06T7/62G16H50/20A61B8/08
CPCG06T7/0012G06T7/62G16H50/20A61B8/08A61B8/52A61B8/5292G06T2207/30056G06T2207/30096A61B8/085A61B8/5223A61B8/483G06N20/00G06V10/454G06V2201/031G06V10/82G06T7/0014G06T2207/10132G06T2207/20081G06T2207/20084
Inventor 粘晓菁林大翔周培廉
Owner 粘曉菁
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