Monday, 08 November 2021
This week, the report writing continued for Chapter 2. Below is the title and summary of the journal that I review in the report.
" In this article, a brain tumour detection system and various anomalies and abnormalities are presented where image pre-processing and preparation include image enhancement, filtering and noise reduction. In this research, the feature selection and integration method are used and the most important statistical features of brain MRI images are used to improve brain tumour detection.The pulsecoupled neural network (PCNN) can be used for image segmentation in the pre-processing stage, especially in the image filtering."
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"In this paper, we present a fully automatic brain tumor segmentation and classification model using a Deep Convolutional Neural Network that includes a multiscale approach. The proposed neural model can analyze MRI images containing three types of tumors: meningioma, glioma, and pituitary tumor, over sagittal, coronal, and axial views and does not need preprocessing of input images to remove skull or vertebral column parts in advance."
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"The tumor in the Brain is the most dangerous disease and can be diagnosed easily and reliably with the help of detection of the tumor with automated techniques on MRI Images. Several methods of efficient diagnosis and segmentation of brain tumors have been suggested by many researchers for effective tumor detection. A review method involving two-stage approaches for 20 research papers published in the period from 2000 to 2020 has been conducted to learn about tumor detection in MRI images. The introduction of quantitative image analysis resulted in fields such as MRI Images. Algorithms and methodologies used to solve specific research problems were included in the results and along with their strengths and limitations."
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"Image segmentation is one of the most challenging techniques in the field of medical image processing. Brain tumor segmentation is emerging technique in this field."
"From the MRI images information about the abnormal tissue growth in the brain is identified. When these algorithms are applied on the MRI images the prediction of brain tumor is done very fast and a higher accuracy helps in providing the treatment to the patients."
"In this work, dicom Magnetic Resonance Image (MRI) is taken as an input and tried to extract tumor cells from the input image. Finally, image thresholding is applied to this image followed by levelset segmentation to extract tumor cells."
