Most commonly, diagnosing the brain hemorrhage-a condition caused by a brain artery busting and causing bleeding is done by medical experts identifying such pathologies from the computer tomography (CT) images. With great advancements in the domain of deep learning, utilizing deep convolutional neural networks (CNN) for such tasks has already proven to achieve encouraging results. One of the major problems of using such an approach is the need for big labeled datasets to train such deep architectures. One of the efficient techniques for training CNNs with smaller datasets is transfer learning. For the efficient use of transfer learning, many parameters are needed to be set, which are having a great impact on the classification performance of the CNN. Most of those parameters are commonly set based on our previous experience or by trial and error. The proposed method addresses the problem of tuning the transfer learning technique utilizing the nature-inspired, population-based metaheuristic Grey Wolf Optimizer (GWO). The proposed method was tested on a small head CT medical imaging dataset. The results obtained from the conducted experiments show that the proposed method outperforms the conventional approach of parameter settings for transfer learning.

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This paper proposes a modified single-objective binary cuckoo search for association rule mining (MBCS-ARM). The proposed algorithm includes a novel representations of individuals, which tackles the problems of large dimensionality with an increasing number of attributes. The MBCS-ARM also supports the mining of rules, where intervals of attributes can either be negative or positive. It uses an objective function composed of support and confidence weighted by two parameters, which control the importance of each measure in the found rules. It is tested on eight publicly available databases, while also compared to several single-objective evolutionary algorithms, and traditional algorithms, all found in the KEEL software tool. The experiments show promising results of the MBCS-ARM, compared to other algorithms, by producing rules, which are interesting, simple, and also easy to understand, which is of great importance in domains like medicine.

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Acute pancreatitis (AP) is a disease with significant morbidity and mortality. The aim of this study was to evaluate the prognostic role of inflammatory markers, particularly interleukins (ILs), in the course of AP and to determine the frequency of etiologic factors of AP. We included patients with AP who were treated at our institution from May 1, 2012 to January 31, 2015. Different laboratory parameters, including ILs, and the severity scoring systems Ranson’s criteria and Bedside Index of Severity in Acute Pancreatitis (BISAP) were analyzed. AP was classified into mild and severe, and independent parameters were compared between these groups. The predictive performance of each parameter was evaluated using receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC). A binomial logistic regression was performed to evaluate Ranson’s criteria and IL6, IL8, and IL10 (at admission and after 48 hours) in the course of AP. Overall, 96 patients were treated, 59 (61.5%) males and 37 (38.5%) females, average age 62.5 ± 16.8 years (range 22–91 years). The best predictor for the severity of AP was IL6, measured 48 hours after admission (AUC = 0.84). Other useful predictors of the severity of AP were lactate dehydrogenase (p < 0.001), serum glucose (p < 0.006), and difference in the platelet count (p < 0.001) between admission and after 48 hours (p < 0.001), hemoglobin (p < 0.027) and erythrocytes (p < 0.029). The major causes of AP were gallstones and alcohol consumption. According to our results, IL6 and Ranson score are important predictors of the severity of AP.