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Examination of Liver cirrhosis Disease using computer tomography images through computer vision

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Abstract:

Automatic liver segmentation not only plays an important role in the analysis of liver disease, but also reduces the cost and humanity’s impact in segmentation. In addition, liver segmentation is a very challenging task due to countless anatomical variations and technical difficulties. Many methods have been designed to overcome these challenges, but these methods still need to be improved to obtain the desired segmentation precision. In this paper, a fast algorithm is proposed for liver extraction from CT images with single-block linear detection. The proposed method does not require iteration; thus, the computational time and complexity are decreased enormously. In addition, the initialization is not crucial in the algorithm, so the algorithm’s robustness and specificity are improved. The experimental evaluation of the proposed method revealed effective segmentation in normal and abnormal (liver haemangioma and liver cancer) abdominal CT images.

 

Existing System:

Typically, the existing mechanisms assumed that the accuracy of prediction was achieved. But this wasn’t the case then, hence, it must be improved further to increase the classification accuracy. Also, other research works addressed these issues by introducing efficient combination. Existing Models based on feature selection and classification raised some issues regarding with training dataset and Test dataset.

 

Limitation:

  • Certain approaches being applicable only for small data.
  • Certain combination of classifier over fit with data set while others are under fit.
  • Some approaches are not adoptable for realtimecollection of database implementation.

 

Proposed System:

In proposed system , we have to import the liver patient dataset CT images. Then the dataset should be pre-processed and remove the anomalies and full up empty cells in the dataset , so the we can further improve the effective Liver diseases detection . Normal and abnormal images

 

Advantages:

  • The performance classification of liver based diseases is further improved.
  • Time complexity and accuracy can measured by various machine learning models ,so that we can measures different .
  • Different machine learning having high accuracy of result.

Risky factors can be predicted early by machine learning models.

 

Block Diagram:

Liver Disease Analysis

 

 

System Requirements:

 

Hardware:

  1. Windows 7,8,10 64 bit
  2. RAM 4GB

Software:

  • Python Idle
  • Numpy
  • Opencv
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  • Price
    Free
  • Instructor pantech team
  • Duration 15 Hrs
  • Enrolled 0 student
  • Access 3 Months

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