WEEK 13

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. 

• Magnetic Resonance Imaging Image Segmentation and Brain Tumor Detection Using Pulse Coupled Neural Network 

" 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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• A Deep Learning Approach for Brain Tumor Classification and Segmentation Using a Multiscale Convolutional Neural Network

"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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• Brain Tumor Detection Analysis Using CNN: A Review 

"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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• Brain Tumor Extraction from MRI Images using MATLAB 

"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." 

• Brain Tumor Detection from MRI Images using Deep Learning Techniques 

"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." 

• Brain Tumor Detection using Image Segmentation Techniques 

"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."