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Optimal biomarker selection using adaptive Social Ski-Driver optimization for liver cancer detection

Gunasekhar P., Vijayalakshmi S.

Biocybernetics and Biomedical Engineering

2020

Vol. 40, no. 4

1611--1625

Most essential biomolecule found in the human body is a biomarker; with these biomarkers, the abnormal biological processes and disease states of each patient can be accurately determined. Nowadays, the biomarker applications are frequently applied during clinical trials to identify cancer patients. In this method, the major significance of miRNA biomarkers during liver cancer detection is analysed. For such analysis, a deep learning technique is introduced along with optimization algorithms. Six different filter-based approaches are considered for feature selection they are Chi-Squared (Chi2), Information Gain (IG), Gain Ratio (GR), Symmetrical Uncertainty (SU), RelieF (RF) and RF-W. Two high ranked features from these selected features are extracted by the Modified Social Ski-Driver optimization (MSSO) algorithm. With that high ranked features, the liver cancer tissues are accurately detected by Sunflower Optimization-based deep neural network (DSFNN) approach. The analysis part concludes that a miRNA biomarker having a higher rank provide better cancer detection results than other low-ranked biomarkers. In this work, 10 different, clinically verified miRNA biomarkers are selected for this detection process. The data required for liver cancer detection is selected from NCBI-GEO database. The performance of this entire cancer detection process is evaluated by accuracy, sensitivity, precision, specificity, and Area under curve (AUC) metrics. Furthermore, we also determined that the usage of 10, 5, and 3 clinically verified miRNAs provide better cancer detection results than other miRNAs. Among all clinically verified miRNAs, the selected three biomarkers (hsa-mir-10b, hsa-let-7c, hsa-mir- 145) has attained higher recognition result. The performance result attained by the proposed DSFNN is compared with five different algorithms for both training and validation datasets.

Impact of low resolution on image recognition with deep neural networks: An experimental study

Koziarski M., Cyganek B.

International Journal of Applied Mathematics and Computer Science

2018

Vol. 28, no. 4

735--744

Due to the advances made in recent years, methods based on deep neural networks have been able to achieve a state-of-the-art performance in various computer vision problems. In some tasks, such as image recognition, neural-based approaches have even been able to surpass human performance. However, the benchmarks on which neural networks achieve these impressive results usually consist of fairly high quality data. On the other hand, in practical applications we are often faced with images of low quality, affected by factors such as low resolution, presence of noise or a small dynamic range. It is unclear how resilient deep neural networks are to the presence of such factors. In this paper we experimentally evaluate the impact of low resolution on the classification accuracy of several notable neural architectures of recent years. Furthermore, we examine the possibility of improving neural networks’ performance in the task of low resolution image recognition by applying super-resolution prior to classification. The results of our experiments indicate that contemporary neural architectures remain significantly affected by low image resolution. By applying super-resolution prior to classification we were able to alleviate this issue to a large extent as long as the resolution of the images did not decrease too severely. However, in the case of very low resolution images the classification accuracy remained considerably affected.