A STUDY ON CLASSIFICATION AND DETECTION OF BRAIN TUMOR TECHNIQUES (original) (raw)

A Survey on Brain Tumor Classification & Detection Techniques

— A cancerous or non-cancerous mass or growth of abnormal cells in the brain. The research shows that in developed countries the main cause of death of people having brain tumor is incorrect detection of brain tumor. The X-ray, CT, MRI is used for initial diagnostic of the cancer. Today Magnetic Resonance Imaging (MRI) is widely used technique for the detection of brain tumor because it provides the more details then CT. The classification of tumor as a cancerous (malignant) or non cancerous (benign) is very difficult task due to the complexity of brain tissue. In this paper, review of various techniques of classification and detection of brain tumor with the use of Magnetic Resonance Image (MRI) is discussed.

Brain Tumor Detection Using Digital Image Processing

2019

According to International Agency for Research on Cancer (IARC), the rate of diagnosis of brain tumor is estimated to be comparatively greater than the mortality rate. Brain tumor is one of the major causes for the increase in Mortality among people. A tumor is an abnormal growth caused by cells reproducing themselves in an uncontrolled manner. Brain tumors are the tenth most common cause of cancer death. Generally, CT scan or MRI that is directed into intracranial cavity produces a complete image of brain. This image is visually examined by the physician for detection and diagnosis of brain tumor. However this method of detection resists the accurate determination of stage and size of tumor. Most Medical Imaging Studies and detection conducted using MRI, Positive Emission Tomography (PET) and Computed tomography (CT) Scan. Brain tumor diagnosis is done by doctors. For detecting brain tumor grading always gives different conclusion between one doctors to another. For helping doctors...

A Review on Performance Evaluation of Image Processing Technique for Brain Tumor Detection

International Journal of Modern Trends in Engineering & Research, 2017

Brain tumor is an uncontrolled growth of tissues in any part of the body. Tumors are of different types and characteristics and have different treatments. Detection of tumor in the earlier stages makes the treatment easier. Presently many medical imaging techniques such as positron emission tomography (PET), X-ray, computed tomography (CT), magnetic resonance imaging (MRI) for tumor detection are in use among which MRI imaging technique is more attractive because of its higher resolution and therefore most preferred technique for diagnosing tumor. MRI scan is safer than any other scan as it does not involve any radiations and gives high resolution of image. Paper gives a brief review of different segmentation and classification methods used for detection of tumor from Magnetic Resonance Images (MRI).

A Brief Review on Brain Tumour Detection and Classifications

Bio-inspired Neurocomputing, 2020

Recognition of unusual pixels in a source brain Magnetic Resonance Image (MRI) remains a difficult activity owing to various similarities with that of surrounding pixels. The presence of brain tumour pixel detection process has been simplified using preprocessing steps before the proposed method starts. The preprocessing steps earns an attempt to enhance the internal pixel regions for improving the brain tumor pixel detection rate. The preprocessing stage may include noise reduction, pixel resolution enhancement, image registration, edge detection methods and artifact detection and reduction. The available techniques in preprocessing stage has different methods for improving the clarity of the source brain MRI image that leads to further processing such as segmentation and classification of tumor images. The proposed work discusses various conventional methods for brain tumour detection and classifications with the limited number of available information.

Review of Brain Tumor Detection using Pattern Recognition Techniques

– Malignant Brain Tumor is one of the most lethal diseases on the Earth. Identifying such a tumor at an early stage is highly necessary in order to treat it properly. Medical imaging plays an important role to detect brain tumors. Although, MRI (Magnetic Resonance Imaging) is often considered to be the most suitable technique to diagnose such a tumor, it has its own limitations. On the other hand, PET (Positron Emission Tomography) has emerged as a more efficient technique to detect a brain tumor both in its pre and post treatment stages. The present work has been carried out with an objective to plan a strategy to identify brain tumors using Artificial Neural Network (ANN) and segmented PET images.

A Review : Brain tumor detection using Digital Image Processing

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2023

Now a day, one of the most common diseases is a brain tumor. The challenge is to identify a tumor at an early stage, which is essential to receiving good care and surviving brain cancer patients. In the human body, the uncontrolled growth of cells is called a brain tumor. They have different types and characteristics and have different treatments. Medical imaging techniques play an important role in the detection of brain tumors. Although MRI (Magnetic Resonance Imaging) is frequently regarded as the best method for identifying this type of tumor, it has several drawbacks, and MRI images are more sensitive to ambient noise and other disruptions. As a result, it is challenging for doctors to identify the tumor and its origin.

Development of Novel Approach for Classification and Detection of Brain Tumor

IJLTEM.COM, 2018

This paper proposed the detection of brain tumor fro m M RI images. The methodology consists of three steps: segmentation, decomposition and classificat ion. Brain tu mor removal and its examination are d ifficult tasks in med ical image processing because brain image and its structure is problematical that can be analyzed only by expert radiologists. In this paper, tumor region detected in the brain using MRI images by a computer-based method. A classification of brain into healthy brain or a brain having a tumor is first done which is then followed by further classification into benign or malignant tumor. An enhancement process is applied to improve the quality of images and limit the risk of distinct regions fusion in the segmentation phase. Then we apply Wavelet Transform in the segmentation process to decompose MRI images. A user friendly Matlab program has been constructed to test the proposed algorithm. A wavelet approach for brain tumor detection and classification through magnetic resonance images has been proposed.

Image Processing Techniques for Brain Tumor Detection : A Review

2015

Volume 4, Issue 5(2), September – October 2015 Page 28 Abstract MRI Imaging play an important role in brain tumor for analysis, diagnosis and treatment planning. It’s helpful to doctor for determine the previous steps of brain tumor. Brain tumor detections are using MRI images is a challenging task, because the complex structure of the brain. Brain tumor is an abnormal growth of cell of brain. MRI images offer better difference concern of various soft tissues of human body. MRI Image provides better results than CT, Ultrasound, and X-ray. In this the various preprocessing, post processing and methods like; (Filtering, contrast enhancement, Edge detection) and post processing techniques like; (Histogram, Threshold, Segmentation, Morphological operation) through image processing (IP) tool is available in MATLAB for detection of brain tumor images (MRI-Images) are discussed.

Critical Analysis of Brain Magnetic Resonance Images Tumor Detection and Classification Techniques

International Journal of Advanced Computer Science and Applications, 2020

The image segmentation, tumor detection and extraction of tumor area from brain MR images are the main concern but time-consuming and tedious task performed by clinical experts or radiologist, while the accuracy relies on their experiences only. So, to overcome these limitations, the usage of computer-aided design (CAD) technology has become very important. Magnetic resonance imaging (MRI) and Computed Tomography (CT) are the two major imaging modalities that are used for brain tumor detection. In this paper, we have carried out a critical review of different image processing techniques of brain MR images and critically evaluate these different image processing techniques in tumor detection from brain MR images to identify the gaps and limitations of those techniques. Therefore, to obtain precise and better results, the gaps can be filled and limitations of various techniques can be improved. We have observed that most of the researchers have employed these stages such as Pre-processing, Feature extraction, Feature reduction, and Classification of MR images to find benign and malignant images. We have made an effort in this area to open new dimensions for the readers to explore the concerned field of research.