Liver Segmentation and Liver Cancer Detection Based on Deep Convolutional Neural Network: A Brief Bibliometric Survey

Background: This study analyzes liver segmentation and cancer detection work, with the perspectives of machine learning and deep learning and different image processing techniques from the year 2012 to 2020. The study uses different Bibliometric analysis methods. Methods: The articles on the topic were obtained from one of the most popular databases- Scopus. The year span for the analysis is considered to be from 2012 to 2020. Scopus analyzer facilitates the analysis of the databases with different categories such as documents by source, year, and county and so on. Analysis is also done by using different units of analysis such as co-authorship, co-occurrences, citation analysis etc. For this analysis Vosviewer Version 1.6.15 is used. Results: In the study, a total of 518 articles on liver segmentation and liver cancer were obtained between the years 2012 to 2020. From the statistical analysis and network analysis it can be concluded that, the maximum articles are published in the year 2020 with China is the highest contributor followed by United States and India. Conclusions: Outcome from Scoups database is 518 articles with English language has the largest number of articles. Statistical analysis is done in terms of different parameters such as Authors, documents, country, affiliation etc. The analysis clearly indicates the potential of the topic. Network analysis of different parameters is also performed. This also indicate that there is a lot of scope for further research in terms of advanced algorithms of computer vision, deep learning and machine learning

Mutable composite firefly algorithm for gene selection in microarray based cancer classification

Cancer classification is critical due to the strenuous effort required in cancer treatment and the rising cancer mortality rate. Recent trends with high throughput technologies have led to discoveries in terms of biomarkers that successfully contributed to cancerrelated issues. A computational approach for gene selection based on microarray data analysis has been applied in many cancer classification problems. However, the existing hybrid approaches with metaheuristic optimization algorithms in feature selection (specifically in gene selection) are not generalized enough to efficiently classify most cancer microarray data while maintaining a small set of genes. This leads to the classification accuracy and genes subset size problem. Hence, this study proposed to modify the Firefly Algorithm (FA) along with the Correlation-based Feature Selection (CFS) filter for the gene selection task. An improved FA was proposed to overcome FA slow convergence by generating mutable size solutions for the firefly population. In addition, a composite position update strategy was designed for the mutable size solutions. The proposed strategy was to balance FA exploration and exploitation in order to address the local optima problem. The proposed hybrid algorithm known as CFS-Mutable Composite Firefly Algorithm (CFS-MCFA) was evaluated on cancer microarray data for biomarker selection along with the deployment of Support Vector Machine (SVM) as the classifier. Evaluation was performed based on two metrics: classification accuracy and size of feature set. The results showed that the CFS-MCFA-SVM algorithm outperforms benchmark methods in terms of classification accuracy and genes subset size. In particular, 100 percent accuracy was achieved on all four datasets and with only a few biomarkers (between one and four). This result indicates that the proposed algorithm is one of the competitive alternatives in feature selection, which later contributes to the analysis of microarray data