One of the imaging techniques which generate better image quality for functional structure of human body is Magnetic resonance imaging (MRI) that is specifically in the case of brain. The MRI has provided better information to both clinical diagnosis and even in research of biomedical. However, the robust and extract MRI image classification can able to generate an enlarged by accomplishment of MRI diagnostic value. The major concern of MRI is extraction, segmentation and detection of an infected area of tumor but the performance of task gets annoyed and consumption of time is high by clinical experts or radiologist whereas their accuracy is totally rely on their experiences. Therefore, the utilization of computer aided technology has become essential for overcoming these disadvantages. Many techniques involving various machine learning algorithms have been proposed to addresses and each has its own drawbacks.In Machine Learning, CNN is used to analyze the image classification and LSTMs is used for text mining and predictions. This research attempts to develop a methodology that leverages the image classification power of CNN with the text mining and predicting power of the LSTM to interpret and predict benign / malignant tumors from MRI images.
Volume 11 | 08-Special Issue
Pages: 497-505