Mining Multilingual and Multiscript Twitter Data: Unleashing the Language and Script Barrier

Of late, in many medical diagnostic applications, automatic detection of brain neoplasm in Magnetic Resonance Imaging (MRI) data is gaining importance. This report presents two improvements for brain neoplasm detection in MRI data: 1) an advanced preprocessing technique to accurately identify the region of interest in MRI data and 2) a hybrid technique using Convolutional Neural Network (CNN) for feature extraction followed by modified Support Vector Machine (SVM) for classification. Toward the advanced preprocessing, contour calibration, Sobel edge detection and contrast optimization, which were ignored in the existing works have been investigated in this work. The autoencoder of CNN model has been used to extract the feature automatically in contrast to the manually defined feature as in the conventional techniques. The learning algorithm for SVM is modified with the addition of a cost function to minimize the false positive prediction addressing the errors in MRI data diagnosis. The proposed approach efficiently detects the presence of neoplasm and also predict whether it is malignant or benign. To check the effectiveness of the proposed preprocessing technique, it was inspected visually and then evaluated using the performance of prediction accuracy. A comparison study between the proposed classification technique and the existing techniques was performed. The result showed that the proposed approach outperformed in terms of accuracy and can handle errors in classification better than the existing approaches.

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Mining Multilingual and Multiscript Twitter Data: Unleashing the Language and Script Barrier

Cited by 2 publications

References 19 publications

Dataset Creation from Multilingual Data of Social Media: Challenges and Consequences

Dataset Creation from Multilingual Data of Social Media: Challenges and Consequences

Computational Intelligence Approach to Improve the Classification Accuracy of Brain Neoplasm in MRI Data

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