Artificial Neural Network Based Lung Cancer Detection for PET/CT Images

M M Ramya,Punithavathy Kannuswami,Sumathi Poobal

doi:10.17485/ijst/2017/v10i42/120342

M M Ramya, Punithavathy Kannuswami + Show 1 more

Open Access

https://doi.org/10.17485/ijst/2017/v10i42/120342

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Abstract

Background/Objectives: To develop an Artificial Neural Networks (ANN) based Computer Aided Diagnosis system (CAD) using texture and fractal features to detect lung cancer from Positron Emission Tomography/Computed Tomography (PET/ CT) images. Methods/Statistical Analysis: Methods such as Wiener filtering and fuzzy image processing were used to suppress noise and improve the contrast respectively in lung PET/CT images. Texture and fractal features were analyzed to extract significant features. ANN with optimal network parameters was designed to train and test the dataset. Total of 1072 training samples and 80 testing samples were used to evaluate the performance of the system. Findings: The proposed fuzzy enhancement played a vital role in improving the detection of lung cancer. 13 significant features were identified (3 texture features and 10 fractal features) for detection of cancerous regions. Proposed method of CAD system yielded better classification accuracy for training and testing with Levenberg-Marquardt back propagation, learning rate = 0.3, momentum = 0.9 and 20 hidden neurons. The training accuracy produced by texture, fractal and combined features were 92.4%, 98.1% and 98.5% respectively. The testing accuracy achieved with the proposed method for texture, fractal and combined features were 91.3%, 95% and 92.5%. Proposed classifier with fractal features yielded a better testing accuracy than texture and combined features. Improvements/Applications: Deep learning algorithms may be implemented to improve the accuracy of the detection. Developed CAD system can act as a decision support system to assist radiologists in lung cancer diagnosis. Keywords: Artificial Neural Networks, Computer Aided Diagnosis, Fuzzy Enhancement, Lung Cancer, Texture and Fractal Features

Highlights

Cancer is a life threatening disease nowadays
PET/CT is currently used in lung cancer diagnosis, staging, treatment planning and monitoring with the combined functional and anatomical information acquired at the same time[6]
This study focuses on establishing a Computer Aided Diagnosis system (CAD) system using Back Propagation Neural Network (BPNN) to analyze the performance of the classifier with the extracted texture, fractal features from lung Positron Emission Tomography/Computed Tomography (PET/ CT) images for lung cancer diagnosis

Summary

Introduction

Cancer is a life threatening disease nowadays. Lung cancer takes the first position in men and second position in women among various cancers. There has been a steady increase in lung cancer occurrence in the last 10 years[1]. The 5 year survival is only 14% and it has not been improved in last few decades[3]. In Indian subcontinent, lung cancer is most often interpreted as tuberculosis and diagnosed very lately in the terminal stages[4]. Diagnosis and better treatment options can improve the patient’s survival time. Image processing techniques play a phenomenal role in lung cancer detection with a potential of diagnosing the disease in its early stages and distinguishing tuberculosis from lung cancer[5]. PET/CT is currently used in lung cancer diagnosis, staging, treatment planning and monitoring with the combined functional and anatomical information acquired at the same time[6].

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