| Title |
Binarized Spiking Neural Networks Optimized with Color Harmony Algorithm for Liver Cancer Classification |
| Authors |
(Pushpa Balakrishnan) ; (B. Baskaran) ; (S. Vivekanandan) ; (P. Gokul) |
| DOI |
https://doi.org/10.5573/IEIESPC.2023.12.6.502 |
| Keywords |
Binarized spiking neural networks; Color harmony algorithm; Color Wiener filtering; Improved non-sub sampled Shearlet transform; Liver cancer |
| Abstract |
Binarized spiking neural networks optimized with a color harmony algorithm for liver cancer classification (BSNN-CHA-LCC) are proposed to classify liver cancer as normal and abnormal. Initially, fusion of an MRI dataset and CT-scan datasets of a liver cancer dataset were taken, and the input images were given to CWF-based preprocessing for removing noise and increasing the quality of input computed tomography (CT) and magnetic resonance imaging (MRI). The preprocessed images of CT and MRI are given to improve the non-sub sampled Shearlet transform (INSST) method-based feature extraction for extracting features. The extracted features were given BSNN to classify liver cancer as normal and abnormal. The proposed method was implemented, and the efficiency of the proposed BSNN-CHA-LCC method was evaluated under performance metrics, such as precision, sensitivity, F-scores, specificity, accuracy, error rate, and computational time. The proposed technique achieved23.03%, 11.56%, and 21.22% higher accuracy and 36.12%, 15.23%, and 27.11% lower error rates than the existing models, such as hybrid-feature analysis depending on machine-learning for liver cancer categorization utilizing fused images (MLP-LCC), Deep learning-based classification of liver cancer histopathology images utilizing only global labels (mask-RCNN-LCC), and deep learning based liver cancer identification utilizing watershed transform and Gaussian mixture method (DNN-GMM-LCC), respectively. |