scholarly journals Segmentation of Brain Tumors from MRI Images Using Convolutional Autoencoder

2021 ◽  
Vol 11 (9) ◽  
pp. 4317
Author(s):  
Milica M. Badža ◽  
Marko Č. Barjaktarović
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Medical Diagnostics ◽  
Magnetic Resonance Images ◽  
Clinical Diagnostics ◽  
Machine Learning Algorithms ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Generalization Ability ◽  
Data Division

The use of machine learning algorithms and modern technologies for automatic segmentation of brain tissue increases in everyday clinical diagnostics. One of the most commonly used machine learning algorithms for image processing is convolutional neural networks. We present a new convolutional neural autoencoder for brain tumor segmentation based on semantic segmentation. The developed architecture is small, and it is tested on the largest online image database. The dataset consists of 3064 T1-weighted contrast-enhanced magnetic resonance images. The proposed architecture’s performance is tested using a combination of two different data division methods, and two different evaluation methods, and by training the network with the original and augmented dataset. Using one of these data division methods, the network’s generalization ability in medical diagnostics was also tested. The best results were obtained for record-wise data division, training the network with the augmented dataset. The average accuracy classification of pixels is 99.23% and 99.28% for 5-fold cross-validation and one test, respectively, and the average dice coefficient is 71.68% and 72.87%. Considering the achieved performance results, execution speed, and subject generalization ability, the developed network has great potential for being a decision support system in everyday clinical practice.

scholarly journals Analysis of machine learning algorithms in brain tumour prediction

2021 ◽  
Vol 2070 (1) ◽  
pp. 012090
Author(s):  
M Gupta ◽  
S K Sharma ◽  
R Saxena ◽  
S Arora
Keyword(s):  
Machine Learning ◽  
Brain Tumour ◽  
Learning Algorithms ◽  
Magnetic Resonance Images ◽  
The Body ◽  
Machine Learning Algorithms ◽  
Primary Concern ◽  
Malignant Brain Tumour ◽  
Structural Consistency ◽  
Cancerous Cells

Abstract The tumour is fundamentally an excessive development of dangerous cells in any part of the body, while a tumour in a brain is an unreasonable development of cancerous cells in the brain. Brain tumour can be either benign or malignant. The benign brain tumour has structural consistency and does not include active (cancer) cells, but the malignant brain tumour has no structure consistency and includes active cells. The primary concern is to segment, detect, and extract the infected tumour area from magnetic resonance images (MRI) which are being performed by radiologists or medical experts, and their accuracy is totally dependent on their experience only. Thus, it becomes very essential to overcome these limitations by the use of artificial intelligence. The current paper uses various machine learning algorithms as well as their features to design a structure to predict brain tumour at an early phase by using different classifiers and comparing their respective accuracy parameters.

scholarly journals The Role of Machine Learning Algorithms for Diagnosing Diseases

2021 ◽  
Vol 2 (01) ◽  
pp. 10-19
Author(s):  
Ibrahim Ibrahim ◽  
Adnan Abdulazeez
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Learning Algorithms ◽  
Medical Diagnostics ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Medical Database ◽  
K Nearest Neighbor ◽  
Medical Sector

Nowadays, machine learning algorithms have become very important in the medical sector, especially for diagnosing disease from the medical database. Many companies using these techniques for the early prediction of diseases and enhance medical diagnostics. The motivation of this paper is to give an overview of the machine learning algorithms that are applied for the identification and prediction of many diseases such as Naïve Bayes, logistic regression, support vector machine, K-nearest neighbor, K-means clustering, decision tree, and random forest. In this work, many previous studies were reviewed that used machine learning algorithms for detecting various diseases in the medical area in the last three years. A comparison is provided concerning these algorithms, assessment processes, and the obtained results. Finally, a discussion of the previous works is presented.

Forecasting US movies box office performances in Turkey using machine learning algorithms

2020 ◽  
Vol 39 (5) ◽  
pp. 6579-6590
Author(s):  
Sandy Çağlıyor ◽  
Başar Öztayşi ◽  
Selime Sezgin
Keyword(s):  
Machine Learning ◽  
Global Economy ◽  
Learning Algorithms ◽  
Forecast Model ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
High Stakes ◽  
Box Office ◽  
Industry Forecast ◽  
The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

Intelligent system of English composition scoring model based on improved machine learning algorithm

2020 ◽  
pp. 1-11
Author(s):  
Jie Liu ◽  
Lin Lin ◽  
Xiufang Liang
Keyword(s):  
Machine Learning ◽  
Evaluation System ◽  
Intelligent System ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Assessment System ◽  
English Composition ◽  
Region Extraction ◽  
Constraint Model

The online English teaching system has certain requirements for the intelligent scoring system, and the most difficult stage of intelligent scoring in the English test is to score the English composition through the intelligent model. In order to improve the intelligence of English composition scoring, based on machine learning algorithms, this study combines intelligent image recognition technology to improve machine learning algorithms, and proposes an improved MSER-based character candidate region extraction algorithm and a convolutional neural network-based pseudo-character region filtering algorithm. In addition, in order to verify whether the algorithm model proposed in this paper meets the requirements of the group text, that is, to verify the feasibility of the algorithm, the performance of the model proposed in this study is analyzed through design experiments. Moreover, the basic conditions for composition scoring are input into the model as a constraint model. The research results show that the algorithm proposed in this paper has a certain practical effect, and it can be applied to the English assessment system and the online assessment system of the homework evaluation system algorithm system.

The Unlearnable Checkerboard Pattern

2019 ◽  
Vol 1 (2) ◽  
pp. 78-80
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Checkerboard Pattern ◽  
Simple Task

Detecting some patterns is a simple task for humans, but nearly impossible for current machine learning algorithms.  Here, the "checkerboard" pattern is examined, where human prediction nears 100% and machine prediction drops significantly below 50%.

1290-P: Gut Microbiota in New-Onset Pediatric Patients with Type 1 Diabetes: Machine Learning Algorithms to Classify Microorganisms Disease-Linked

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1290-P
Author(s):  
GIUSEPPE D’ANNUNZIO ◽  
ROBERTO BIASSONI ◽  
MARGHERITA SQUILLARIO ◽  
ELISABETTA UGOLOTTI ◽  
ANNALISA BARLA ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 1 Diabetes ◽  
Gut Microbiota ◽  
Pediatric Patients ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
New Onset
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