scholarly journals A survey on detection of brain tumor using computer vision and machine learning technique

2021 ◽  
Vol 7 (3) ◽  
pp. 84-88
Author(s):  
Pranav Shetty ◽  
Suraj Singh ◽  
Rasvi Jambhulkar ◽  
Kajal Sheth ◽  
Deepali Ujalambkar
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Brain Tumor ◽  
Machine Learning Technique ◽  
Learning Technique

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.

NeXt for neuro-radiosurgery: A fully automatic approach for necrosis extraction in brain tumor MRI using an unsupervised machine learning technique

2017 ◽  
Vol 28 (1) ◽  
pp. 21-37 ◽  
Author(s):  
Leonardo Rundo ◽  
Carmelo Militello ◽  
Andrea Tangherloni ◽  
Giorgio Russo ◽  
Salvatore Vitabile ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Machine Learning Technique ◽  
Unsupervised Machine Learning ◽  
Learning Technique ◽  
Fully Automatic ◽  
Tumor Mri

Prediction of brain tumor progression using a machine learning technique

2010 ◽  
Author(s):  
Yuzhong Shen ◽  
Debrup Banerjee ◽  
Jiang Li ◽  
Adam Chandler ◽  
Yufei Shen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Tumor Progression ◽  
Machine Learning Technique ◽  
Learning Technique

scholarly journals The Development of a Quantitative Precipitation Forecast Correction Technique Based on Machine Learning for Hydrological Applications

Atmosphere ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 111 ◽  
Author(s):  
Chul-Min Ko ◽  
Yeong Yun Jeong ◽  
Young-Mi Lee ◽  
Byung-Sik Kim
Keyword(s):  
Machine Learning ◽  
Heavy Rainfall ◽  
Extreme Rainfall ◽  
Machine Learning Techniques ◽  
Precipitation Forecast ◽  
Machine Learning Technique ◽  
Rainfall Forecast ◽  
Quantitative Precipitation Forecast ◽  
Correction Technique ◽  
Learning Technique

This study aimed to enhance the accuracy of extreme rainfall forecast, using a machine learning technique for forecasting hydrological impact. In this study, machine learning with XGBoost technique was applied for correcting the quantitative precipitation forecast (QPF) provided by the Korea Meteorological Administration (KMA) to develop a hydrological quantitative precipitation forecast (HQPF) for flood inundation modeling. The performance of machine learning techniques for HQPF production was evaluated with a focus on two cases: one for heavy rainfall events in Seoul and the other for heavy rainfall accompanied by Typhoon Kong-rey (1825). This study calculated the well-known statistical metrics to compare the error derived from QPF-based rainfall and HQPF-based rainfall against the observational data from the four sites. For the heavy rainfall case in Seoul, the mean absolute errors (MAE) of the four sites, i.e., Nowon, Jungnang, Dobong, and Gangnam, were 18.6 mm/3 h, 19.4 mm/3 h, 48.7 mm/3 h, and 19.1 mm/3 h for QPF and 13.6 mm/3 h, 14.2 mm/3 h, 33.3 mm/3 h, and 12.0 mm/3 h for HQPF, respectively. These results clearly indicate that the machine learning technique is able to improve the forecasting performance for localized rainfall. In addition, the HQPF-based rainfall shows better performance in capturing the peak rainfall amount and spatial pattern. Therefore, it is considered that the HQPF can be helpful to improve the accuracy of intense rainfall forecast, which is subsequently beneficial for forecasting floods and their hydrological impacts.

Voice Pathology Detection Using Machine Learning Technique

2020 ◽  
Author(s):  
Fahad Taha AL-Dhief ◽  
Nurul Mu'azzah Abdul Latiff ◽  
Nik Noordini Nik Abd. Malik ◽  
Naseer Sabri ◽  
Marina Mat Baki ◽  
...  
Keyword(s):  
Machine Learning ◽  
Machine Learning Technique ◽  
Learning Technique ◽  
Voice Pathology Detection

Evaluation of caridean ecological distribution in the Ubatuba region, southeastern Brazilian coast using unsupervised machine learning technique

2021 ◽  
Author(s):  
Alexandre Oliveira Marques ◽  
Aline Nonato Sousa ◽  
Veronica Pereira Bernardes ◽  
Camila Hipolito Bernardo ◽  
Danielle Monique Reis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brazilian Coast ◽  
Ecological Distribution ◽  
Machine Learning Technique ◽  
Unsupervised Machine Learning ◽  
Learning Technique
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