scholarly journals The Profile of Brain Tumor Cases in RSUD Dr Soetomo, Surabaya

2021 ◽  
Vol 5 (2) ◽  
pp. 49-54
Author(s):  
R. Yuvasanghar Ravindra Mohan ◽  
Joni Wahyuhadi ◽  
Ni Wajan Tirthaningsih
Keyword(s):  
Brain Tumor ◽  
Benign Tumor ◽  
Data Distribution ◽  
Neurological Symptoms ◽  
Tumor Type ◽  
Screening Process ◽  
Adult Age ◽  
And Gender ◽  
Functional Neurological Symptoms ◽  
The Brain

Brain tumor is a condition affecting many people worldwide.Patients often had similar functional neurological symptoms even if the type of tumors diagnosed were entirely different at a later stage.Some of the neurological symptoms were tingling in the feet, changes in eyesight,tremors in the extremities, headaches or migraines and more. Patients who suffer from brain tumor go through a screening process to identify the cause of the problem. Factors such as age and gender was observed in this study which affected the data distribution of brain tumors. This study aimed to find the data distribution of the brain tumor cases in RSUD Dr Soetomo. The results obtained showed that the highest number of brain tumor found were meningiomas followed by unspecified brain neoplasms as the second highest and adenomas being the third highest tumor type found in RSUD Dr Soetomo. The total number of female patients with brain tumor were higher than that of males. The median age of the patients was found to be 45. The highest number of tumor cases were seen in the adult age group followed by teens, children and elderly. The benign tumor type is found to be higher in number as compared to malignant. The number of supratentorial tumors are also found to be higher than infratentorial overall. Keywords: age; gender; brain tumor

scholarly journals Survey on Brain Tumor Identification

2018 ◽  
Vol 7 (3.12) ◽  
pp. 218
Author(s):  
C Malathy ◽  
Namrata Kundu ◽  
Sayan Sadhukhan
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Malignant Tumor ◽  
Benign Tumor ◽  
Benign Tumors ◽  
Tumor Identification ◽  
Complex Organization ◽  
Abnormal Cells ◽  
The Cost ◽  
The Brain

Brain tumors are caused by the growth of abnormal cells inside the Brain. Brain tumor can be classified as Benign (non cancerous) and malignant(cancerous). Malignant brain tumors usually grow rapidly when compared to benign tumors, and aggressively spread and affect the surrounding tissues. Detection of tumor in brain can turn out to be cumbersome, owing to the complex organization of the Brain. The cost of making an error in Identifying a Malignant Tumor from a Benign Tumor is too high. At a time, when cases of Brain Tumors are growing, mostly among people of age between 65 and 79, but not just confined to that age bracket, we can take advantage of the advancement in the field of technology and accurately identify tumors and help save lives.   

scholarly journals Image Based Group Classifier for Brain Tumor Detection Using Machine Learning Technique

2020 ◽  
Vol 37 (5) ◽  
pp. 865-871
Author(s):  
Putta Rama Krishnaveni ◽  
Gattim Naveen Kishore
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Brain Mri ◽  
The United States ◽  
Tumor Detection ◽  
Tumor Type ◽  
Machine Learning Technique ◽  
Learning Technique ◽  
The Brain

In view of insights of the Central Brain Tumor Registry of the United States (CBTRUS), brain tumor is one of the main sources of disease related deaths in the World. It is the subsequent reason for tumor related deaths in adults under the age 20-39. Magnetic Resonance Imaging (MRI) is assuming a significant job in the examination of neuroscience for contemplating brain images. The investigation of brain MRI Images is useful in brain tumor analysis process. Features will be extricated and selected from the segmented pictures and afterward grouped by utilizing the classification procedures to analyze whether the patient is ordinary (having no tumor) or irregular (having tumor). One of the most dangerous cancers is brain tumor or cancer which affects the human body's main nervous system. Infection that can affect is very sensitive to the brain. Two types of brain tumors are present. The tumor may be categorized as benign and malignant. The benign tumor represents a change in the shape and structure of the cells, but cannot contaminate or spread to other cells in the brain. The malignant tumor can spread and grow if not carefully treated and removed. The detection of brain tumors is a difficult and sensitive task involving the classifier's experience. In the proposed work a Group based Classifier for Brain Tumor Recognition (GbCBTD) is introduced for the efficient segmentation of MRI images and for identification of tumor. The use of Convolutional Neural Network (CNN) system to classify the brain tumor type is presented in this work. Relevant features are extracted from images and by using CNN with machine learning technique, tumor can be recognized. CNN can reduce the cost and increase the performance of brain tumor detection. The proposed work is compared to the traditional methods and the results show that the proposed method is effective in detecting tumors.

scholarly journals Detection and classification of brain tumor using machine learning approaches

2019 ◽  
Vol 10 (3) ◽  
pp. 2153-2162
Author(s):  
Pushpa B R ◽  
Flemin Louies
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Extraction Methods ◽  
Heterogeneous Data ◽  
Tumor Type ◽  
Learning Approaches ◽  
Morphological Operation ◽  
Multiple Feature ◽  
The Brain

This paper proposes a method where a framework is constructed to detect and classify the tumor type. Over a period of years, many researchers have been researched and proposed methods in this space. We have proposed a method that is capable of analyzing the heterogeneous data and classifies tumor type. MRI images have been considered for this project since it gives the clear structure of the brain, without any surgery it scans and gives the structure of the brain this helps in further processing in the detection of the tumor. Human prediction in classifying the tumor from the MRI leads to misclassification. This motivates our project to construct the algorithm to predict the tumor. Machine learning plays a key role in predicting tumor. In this proposed paper, we have constructed a framework for detecting the brain tumor and classifying its type. The approach goes under pre-processing to filter and smooth the image. The segmentation is carried out by using morphological operation followed by masking, which increases the accuracy in the classification step. The multiple feature extraction methods are utilized to extract the feature from the masked image, and for classification, the kernel SVM is used.

scholarly journals AUTOMATIC SEGMENTATION AND CLASSIFICATION OF BRAIN TUMOR USING DEEP LEARNING

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
K.Ganga Durga Prasad ◽  
A.J.N. Murthy ◽  
G Narasimha ◽  
New Sinha
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Convolutional Neural Networks ◽  
Automatic Segmentation ◽  
Tumor Detection ◽  
Tumor Type ◽  
The Brain

The brain tumors, are the maximum not unusual place and threatening disease, main to a totally quick lifestyles of their maximum grade. Thus, remedy making plans is a key level to enhance the lifestyles of sufferers. Normally, distinct photo strategies which includes CT, MRI and ultrasound photo are used to hit upon the tumor in a brain. on this approach MRI photos are used to diagnose brain tumor guide type of tumor vs non-tumor is a tough challenge for radiologosts. we gift an approach for detection and type of tumors with inside the brain. The computerized brain tumor type could be very hard challenge in brain tumor. In this approach, computerized brain tumor detection is executedwith the aid of usingthe use of Convolutional Neural Networks (CNN) type.Our proposed automation gadgetcould take an MRI and examine it to locate bengin (non-cancerous) or malignant (cancerous).

scholarly journals Detection of Brain Tumor using KNN and LLOYED Clustering

2020 ◽  
Vol 9 (3) ◽  
pp. 1247-1250
Keyword(s):  
Brain Tumor ◽  
Wavelet Transform ◽  
Benign Tumor ◽  
Detection Methods ◽  
Mr Image ◽  
Correct Analysis ◽  
Novel Approach ◽  
Life Threatening ◽  
Abnormal Cells ◽  
The Brain

Today world the brain tumor is life threatening and the main reason for the death. The growth of abnormal cells in brain leads to brain tumor. Brain tumor is categorized into malignant tumor and benign tumor. Malignant is cancerous whereas Benign tumor is non-cancerous. Diagnosing at earlier stage can save the person. It is actually a great challenge to find the brain tumor and classifying its type. Detection of Brain Tumor and the correct analysis of the Tumor structure is difficult task. To overcome the drawbacks of exiting brain tumor detection methods the proposed system is presented using KNN & LLOYED clustering. Undoubtedly, this saves the time as well as it gives more accurate results as in comparison to manual detection. The proposed method is a novel approach for detection Tumor along with the ability to calculate the area (%age) occupied by the Tumor in the overall brain cells. Firstly, Tumor regions from an MR image are segmented using an OSTU Algorithm. KNN& LLOYED are used for detecting as well as distinguishing Tumor affected tissues from the not affected tissues. Total twelve features are extracted like correlation, contrast, energy, homogeneity etc. by performing “wavelet transform on the converted gray scale image”. For feature extraction DB5 wavelet transform is used.

A Survey on Detection and Classification of Brain Tumor Using Image Processing Techniques

2020 ◽  
pp. 967-973
Author(s):  
V. Deepika ◽  
T. Rajasenbagam
Keyword(s):  
Image Processing ◽  
Brain Tumor ◽  
Soft Tissues ◽  
Tumor Detection ◽  
Tumor Classification ◽  
Normal Brain ◽  
Image Processing Techniques ◽  
Normal Brain Function ◽  
Processing Techniques ◽  
The Brain

A brain tumor is an uncontrolled growth of abnormal brain tissue that can interfere with normal brain function. Although various methods have been developed for brain tumor classification, tumor detection and multiclass classification remain challenging due to the complex characteristics of the brain tumor. Brain tumor detection and classification are one of the most challenging and time-consuming tasks in the processing of medical images. MRI (Magnetic Resonance Imaging) is a visual imaging technique, which provides a information about the soft tissues of the human body, which helps identify the brain tumor. Proper diagnosis can prevent a patient's health to some extent. This paper presents a review of various detection and classification methods for brain tumor classification using image processing techniques.

Novel Phospholipid-Based Labrasol Nanomicelles Loaded Flavonoids for Oral Delivery with Enhanced Penetration and Anti-Brain Tumor Efficiency

2020 ◽  
Vol 17 (3) ◽  
pp. 229-245
Author(s):  
Gang Wang ◽  
Junjie Wang ◽  
Rui Guan
Keyword(s):  
Drug Delivery ◽  
Brain Tumor ◽  
Oral Delivery ◽  
Safe Delivery ◽  
Drug Delivery Vehicles ◽  
Therapeutic Benefits ◽  
Delivery Vehicles ◽  
The Brain

Background: Owing to the rich anticancer properties of flavonoids, there is a need for their incorporation into drug delivery vehicles like nanomicelles for safe delivery of the drug into the brain tumor microenvironment. Objective: This study, therefore, aimed to prepare the phospholipid-based Labrasol/Pluronic F68 modified nano micelles loaded with flavonoids (Nano-flavonoids) for the delivery of the drug to the target brain tumor. Methods: Myricetin, quercetin and fisetin were selected as the initial drugs to evaluate the biodistribution and acute toxicity of the drug delivery vehicles in rats with implanted C6 glioma tumors after oral administration, while the uptake, retention, release in human intestinal Caco-2 cells and the effect on the brain endothelial barrier were investigated in Human Brain Microvascular Endothelial Cells (HBMECs). Results: The results demonstrated that nano-flavonoids loaded with myricetin showed more evenly distributed targeting tissues and enhanced anti-tumor efficiency in vivo without significant cytotoxicity to Caco-2 cells and alteration in the Trans Epithelial Electric Resistance (TEER). There was no pathological evidence of renal, hepatic or other organs dysfunction after the administration of nanoflavonoids, which showed no significant influence on cytotoxicity to Caco-2 cells. Conclusion: In conclusion, Labrasol/F68-NMs loaded with MYR and quercetin could enhance antiglioma effect in vitro and in vivo, which may be better tools for medical therapy, while the pharmacokinetics and pharmacodynamics of nano-flavonoids may ensure optimal therapeutic benefits.

A Review of Various Machine Learning Techniques for Brain Tumor Detection from MRI Images

2020 ◽  
Vol 16 (8) ◽  
pp. 937-945
Author(s):  
Aaishwarya Sanjay Bajaj ◽  
Usha Chouhan
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Ct Scan ◽  
Imaging Techniques ◽  
Critical Evaluation ◽  
Tumor Detection ◽  
Machine Learning Techniques ◽  
X Rays ◽  
Detection Techniques ◽  
The Brain

Background: This paper endeavors to identify an expedient approach for the detection of the brain tumor in MRI images. The detection of tumor is based on i) review of the machine learning approach for the identification of brain tumor and ii) review of a suitable approach for brain tumor detection. Discussion: This review focuses on different imaging techniques such as X-rays, PET, CT- Scan, and MRI. This survey identifies a different approach with better accuracy for tumor detection. This further includes the image processing method. In most applications, machine learning shows better performance than manual segmentation of the brain tumors from MRI images as it is a difficult and time-consuming task. For fast and better computational results, radiology used a different approach with MRI, CT-scan, X-ray, and PET. Furthermore, summarizing the literature, this paper also provides a critical evaluation of the surveyed literature which reveals new facets of research. Conclusion: The problem faced by the researchers during brain tumor detection techniques and machine learning applications for clinical settings have also been discussed.

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