Liver fibrosis staging using CT image texture analysis and soft computing

Three-way decision support for diagnosis on focal liver lesions

“…Kayaalti et al also proposed a method to distinguish liver fibrosis stage based on texture features of liver CT images [26]. The text features are combinatorial and obtained from multiple methods, which include gray level co-occurrence matrix, discrete wavelet transform and discrete Fourier transform.…”

“…Finally, the FLLs are judged as benign or cancerous using classification algorithms. The most commonly used classification algorithms include Neural Networks (NN) [44,58,52], k-Nearest Neighbors (KNN) [38,11], Support Vector Machine (SVM) [55,56,5], Decision Trees [1,18] and the combination of multiple classification algorithms [57,26].…”

Study of Image Analysis Algorithms for Segmentation, Feature Extraction and Classification of Cells

“…Some researches use intelligent algorithms to classify the cell objective (dos Santos et al 2015;Glezakos et al 2010;Keltch, Lin, and Bayrak 2014), soft computing (Kayaaltı et al 2014;Çalişir and Dogantekin 2011), k-means (Ashcroft et al 2011), decision tree (Leonard et al 2015) or a combination of two or more techniques (Li et al 2012;Huang and Murphy 2004). One case is presented in (Dogantekin, Avci, and Erkus 2013), where authors proposed in the classification stage an Adaptive Network Based on Fuzzy Inference System (ANFIS) to automatically classify RNA viruses.…”

“…(Liu and Liu 2013), , (Wang et al 2006), (Dogantekin, Avci, and Erkus 2013), (Ong and Chandran 2005), (Gertych et al 2015), (Abeysekera et al 2014), (Mao et al 2014), (dos Santos et al 2015), (Kayaaltı et al 2014), (Stoklasa, Majtner, and Svoboda 2014) Wavelet features.…”

“…(Filipczuk, Krawczyk, and Woźniak 2013), (Ashcroft et al 2011), (dos Santos et al 2015, (Kayaaltı et al 2014), (Glezakos et al 2010), (Keltch, Lin, and Bayrak 2014), (Sagonas et al 2013) Boosting Joint boosting, gentle boosting, adaptive boosting.…”