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Image Processing and Machine Learning-Based Classification and Detection of Liver Tumor.

V Durga Prasad Jasti¹, Enagandula Prasad², Manish Sawale³

¹CSE Department, VR Siddhartha Engineering College, Andhra Pradesh, India.

Biomed Research International

|August 5, 2022

Summary

This summary is machine-generated.

This study introduces an image processing and machine learning technique for classifying liver cancer. The method uses fuzzy histogram equalization and machine learning algorithms for accurate identification of liver tumors.

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Area of Science:

Hepatology
Medical Imaging
Machine Learning

Background:

The liver performs vital functions including digestion, detoxification, and nutrient metabolism.
Failure in cell regeneration and removal of damaged cells can lead to liver tumors, categorized as benign or malignant.
Malignant liver tumors pose a significant health risk.

Purpose of the Study:

To present an image processing and machine learning-based technique for liver cancer classification and identification.
To develop a method for distinguishing between benign and malignant liver tumors using computational approaches.

Main Methods:

Image preprocessing using fuzzy histogram equalization to reduce noise.
Image segmentation to isolate regions of interest.
Classification using Radial Basis Function-Support Vector Machine (RBF-SVM), Artificial Neural Network (ANN), and Random Forest algorithms.

Main Results:

The proposed technique effectively classifies and identifies liver tumors.
Different machine learning models (RBF-SVM, ANN, Random Forest) were evaluated for their performance in liver cancer detection.
Image processing techniques were crucial for preparing data for machine learning models.

Conclusions:

The developed image processing and machine learning approach shows promise for accurate liver cancer diagnosis.
This technique can aid in the early and precise identification of liver tumors, potentially improving patient outcomes.
Further research can explore advanced algorithms and larger datasets to enhance diagnostic accuracy.