Image processing plays a major role in neurologists' clinical diagnosis in the medical field. Several types of imagery are used for diagnostics, tumor segmentation, and classification. Magnetic resonance imaging (MRI) is favored among all modalities due to its noninvasive nature and better representation of internal tumor information. Indeed, early diagnosis may increase the chances of being lifesaving. However, the manual dissection and classification of brain tumors based on MRI is vulnerable to error, time‐consuming, and formidable task. Consequently, this article presents a deep learning approach to classify brain tumors using an MRI data analysis to assist practitioners. The recommended method comprises three main phases: preprocessing, brain tumor segmentation using k‐means clustering, and finally, classify tumors into their respective categories (benign/malignant) using MRI data through a finetuned VGG19 (i.e., 19 layered Visual Geometric Group) model. Moreover, for better classification accuracy, the synthetic data augmentation concept i s introduced to increase available data size for classifier training. The proposed approach was evaluated on BraTS 2015 benchmarks data sets through rigorous experiments. The results endorse the effectiveness of the proposed strategy and it achieved better accuracy compared to the previously reported state of the art techniques.
The choice of relevant techniques in preprocessing, segmentation and feature extraction is very efficient and effective in rate of online handwriting recognition system. This paper presents a novel deductive method for detecting critical points of the Persian/Arabic handwritten character system in all their different shapes. The implemented method has increased the performance rate of the online Persian/Arabic handwritten recognition system and has decreased the computational mistake for finding critical points. This method helps us to extract stroke of each online handwritten letter and then divided each stroke into some parts, i.e. tokens. The minimal features set are collected from these tokens and encoding to a classifier. The neural network classifier is designed with a robust weight initialization method. Finally, a database set of the Persian handwritten character samples has been employed to test the system in all their different shapes.
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