Lung Cancer Diagnosis Based on Chan-Vese Active Contour and Polynomial Neural Network
Kamel Hussien Rahouma, Shahenda Mahmoud Mabrouk, Mohamed Aouf · Procedia Computer Science · 2021
Lung cancer is of the most serious and common type of cancers. It is usually diagnosed in the final stages which make it hard to treat. Now days, many techniques are to help in the detection of lung cancer, but still there is further need to develop more systems with more improvement and higher accuracy for better detection. In this research work, we introduce a computer-aided detection (CAD) system using computed tomography (CT) scans for nodule classification. The proposed system is divided into four stages that involve image pre-processing by using Gabor filter and Kuwahara filter, image segmentation by applying Chan- Vese active contouring. Feature extraction where features are computed using Discrete Wavelet Transform (DWT) at one, two and three levels of decomposition. After that, 13 features are computed from each wavelet sub-band. As a result the output features are compared and the best output is used to train Polynomial Neural Network (PNN) classification method to classify benign and malignant nodules. The result of the proposed system shows high performance in both the segmentation and the classification with accuracy of 96.66% for the classifying method.