Development of breast cancer diagnosis system based on fuzzy logic and probabilistic neural network

Taha Mohammed Hasan,Sahab Dheyaa Mohammed,Jumana Waleed

doi:10.15587/1729-4061.2020.202820

Abstract

Breast cancer is one of the most common kinds of cancers that infect females in the whole world. It has happened when the cells in breast tissues start to grow in an uncontrollable way. Because it leads to death, early detection and diagnosis is a very important task to save the patient's life. Due to the restriction of human observers, computer plays a significant role in detecting early cancer signs. The proposed system uses a multi-resolution analysis and a top-hat operation for detecting the suspicious regions in a mammogram image. The discrete wavelet transform feature analysis is utilized for extracting features from the region of interest. Fuzzy Logic (FL) and Probabilistic Neural Network (PNN) are utilized for classifying the tumor into normal or abnormal. The differences between the proposed system and other researches are the use of adaptive threshold value depending on each image, by using Discrete Wavelet Transform (DWT) in both segmentation and feature extraction phases, which decrease complexity and time. Additionally, the detection of more than one tumor in the breast mammogram image and the utilization of FL and PNN work on increasing the system efficiency that led to raising the accuracy rate of the system and reducing the time. The obtained results of accuracy, sensitivity, and specificity were equal to 99 %, 98 %, and 47 %, respectively, and these results showed that the proposed system is more accurate than the other previous related works

Highlights

In the last few decades, cancer is one of the most critical and deadly diseases all over the world
The aim of this study is to introduce a hybrid approach for breast cancer diagnosis based on FL and PNN to help the specialists to specify suspicious regions in the breast mammogram images
The dataset used in this proposed system is the Mammographic Image Analysis Society (MIAS) Database (includes breast images for both sides (arranged in pairs films, where each pair represents the right mammograms and left of a single patient) belonging to 81 patients, which means 163 images of size 1,024×1,024, each one belongs to one of normal, benign or malignant and distributed as: 80 normal images, 40 benign images, and 43 malignant images

Summary

Introduction

In the last few decades, cancer is one of the most critical and deadly diseases all over the world. When the normal cell becomes old, it shrivels until die, new cells will be created This process does not follow a normal way, some new cells are created when they are not needed anymore, and old cells don’t die to let the new cells to replace them. This uncommon creation of cells forms a chunk of tissue, called a tumor, lump, or growth. According to the World Health Organization (WHO), in 2015, there were (8.8 million) deaths of cancer. The recent existing studies assured that breast cancer represents 18 % of all kinds of female cancers and the 5th cause of death worldwide [2]

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Results

Conclusion