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MultiTumor Analyzer (MTA-20–55): A network for efficient classification of detected brain tumors from MRI images

Sahoo Akshya Kumar, Parida Priyadarsan, Panda Manoj Kumar, Muralibabu K., Mohanty Ashima Sindhu

Biocybernetics and Biomedical Engineering

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2024

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Vol. 44, no. 3

617--634

EN

Brain cancer, one of the leading causes of mortality worldwide, is caused by brain tumors. Early diagnosis of tumors and predicting their progression can help doctors to save lives. In this article, we have designed an automated approach for locating and classifying tumors from MRI images. The novelties of the research work include the following two stages: Developing an encoder-decoder type 20-Layered deep neural network (DNN) named MultiTumor Analyzer (MTA-20) with 15 down-sampling layers and 4 up-sampling layers, the segmentation is performed in the initial stage. Here, we have adhered a Leaky ReLU activation function instead of ReLU which learn a parameter with negative values that may have valuable information which is essential specifically for image segmentation. Further, a 55-layered DNN using multistage feature fusion is developed in the second stage of the work for the classification of localized tumors. The classification is performed using developed MultiTumor Analyzer (MTA-55) DNN with Softmax classifier. The efficacy of the designed network is validated using highly cited quantitative measures such as accuracy, sensitivity, specificity, dice similarity coefficient (DSC), precision, and F1-measure. It is observed that the proposed MTA-20 DNN attains the average accuracy, sensitivity, specificity, DSC, and precision of 99.2 %, 94.6 %, 99.3 %, 88 %, and 82.5 % respectively against seven state-of-the-art techniques. Also, it is found that, the proposed MTA-55 DNN provides the overall accuracy, recall, specificity, F1-measure, precision, and DSC of 99.8 %, 99.633 %, 99.844 %, 99.659 %, 99.689 %, and 99.656 % respectively as compared to thirteen state-of-the-art techniques. These results corroborate the superiority of the proposed technique.

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Selection of an industrial robot for assembly jobs using multi-criteria decision making methods

Suszyński Marcin, Rogalewicz Michał

Management and Production Engineering Review

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2020

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Vol. 11, No. 1

62--72

EN

The paper proposes three multi-criteria decision-making (MCDM) methods for the selection of an industrial robot for a universal, flexible assembly station, taking into consideration the technical and performance parameters of the robot. Fuzzy versions of AHP and TOPSIS methods as well as SMART were chosen from the variety of MCDM methods as they represent different attitudes to analysis. In order to minimise the impact of the method applied on the final decision, a list of results of the analyses has been developed and a final classification has been made based on decision makers’ preferences concerning selected parameters of the robot

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Measuring thermal performance of gravitational heat pipes depending on the amount of working fluid

Jobb M., Nemec P., Kosa L., Malcho M.

Structure and Environment

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2014

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Vol. 6, no. 1

43--45

EN

This paper deals with an impact of working medium on the performance parameters of the heat pipe. The aim was to find the ideal amount of working fluid used in gravitational heat pipes. For the working medium 96% alcohol was selected.