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دسترسی آزاد مقاله
1 - طبقه بندی بهینه تومورهای مغزی در تصاویر MRI به کمک تکنیک¬های یادگیری عمیق
زهره عربی امید مهدی یار مهدی تقی زادهفناوری های تصویربرداری پزشکی و بیولوژیک، اطلاعات تصویری ارزشمندی از ساختار و عملکرد یک ارگان را از سطح مولکول¬ها تا کل جسم فراهم می کنند. مغز پیچیده ترین عضو در بدن است و با توسعه سریع فناوری های تصویربرداری پزشکی و بیولوژیکی، توجهات تحقیقاتی فزاینده ای را به خود جلب می چکیده کاملفناوری های تصویربرداری پزشکی و بیولوژیک، اطلاعات تصویری ارزشمندی از ساختار و عملکرد یک ارگان را از سطح مولکول¬ها تا کل جسم فراهم می کنند. مغز پیچیده ترین عضو در بدن است و با توسعه سریع فناوری های تصویربرداری پزشکی و بیولوژیکی، توجهات تحقیقاتی فزاینده ای را به خود جلب می کند. از شایع ترین بیماری های مغز می توان به ایجاد بافت ناهنجار در سلول های مغزی اشاره کرد که منجر به تشکیل تومورهای مغزی می شود. از آنجایی که تومورهای مغزی با خطر مرگ و میر قابل توجهی مرتبط هستند و پیش بینی دقیق و سریع این بیماری در روند درمان تاثیر مستقیم دارد، لذا در این تحقیق از تعداد زیادی داده های تصویربرداری MRI تومور مغزی برای شناسایی سرطان های مغز و یافتن روشی با استفاده از تکنیک های یادگیری عمیق استفاده شد. برای تشخیص خودکار از چند مدل یادگیری عمیق استفاده شد و طبقه‌بندی سه نوع تومور مغزی، متشکل از گلیوم، مننژیوم و هیپوفیز نیزبا این الگوریتم ها انجام شد. بر اساس نتایج آزمون‌های انجام‌شده، بهترین دقت نتایج به‌دست‌آمده در این تحقیق ۹۶ درصد بود که با در نظر گرفتن نسبت 60 درصد برای داده‌های آموزشی و 40 درصد برای داده‌های آزمون حاصل شد. پرونده مقاله -
دسترسی آزاد مقاله
2 - تشخیص تومور مغزی در تصاویر رزونانس مغناطیسی با استفاده از شبکه عصبی کانولوشنی عمیق
میترا افسری نژاد نبي اله شیری رامین براتیدر این مقاله، تشخیص تومور مغز از طریق به کارگیری تکنیک‌های پیشرفته یادگیری عمیق مورد بررسی قرار گرفته است. رویکرد این مطالعه شامل توسعه و آموزش یک معماری جامع از شبکه عصبی کانولوشنی (CNN) با بهره‌گیری از یک مجموعه داده گسترده از تصاویر رزونانس مغناطیسی مغز (MRI) می¬باشد چکیده کاملدر این مقاله، تشخیص تومور مغز از طریق به کارگیری تکنیک‌های پیشرفته یادگیری عمیق مورد بررسی قرار گرفته است. رویکرد این مطالعه شامل توسعه و آموزش یک معماری جامع از شبکه عصبی کانولوشنی (CNN) با بهره‌گیری از یک مجموعه داده گسترده از تصاویر رزونانس مغناطیسی مغز (MRI) می¬باشد، مدل پیشنهادی در طبقه‌بندی بافت معمولی مغز و مناطق تحت تأثیر تومور بسیار توانمند است. این معماری شامل لایه‌های متعدد از جمله لایه‌های کانولوشنی، نرمال‌سازی دسته‌ای و لایه‌های پولینگ است که در نهایت به یک لایه قوی طبقه‌بندی منجر می‌شود. از طریق آموزش دقیق و بهینه‌سازی، شبکه عصبی کانولوشنی معرفی‌شده توانسته است در طبقه‌بندی تومور مغز به دقت بالایی دست یابد. اثربخشی این مدل پیشنهادی از طریق آزمایشات جامع به نمایش گذاشته شده که نشان‌دهنده قابلیت آن در تشخیص دقیق تومور مغز است. پرونده مقاله -
دسترسی آزاد مقاله
3 - A Novel Fuzzy-C Means Image Segmentation Model for MRI Brain Tumor Diagnosis
Aref Safari Danial Barazandeh Seyed Ali Khalegh PourAccurate segmentation of brain tumor plays a key role in the diagnosis of brain tumor. Preset and precise diagnosis of Magnetic Resonance Imaging (MRI) brain tumor is enormously significant for medical analysis. During the last years many methods have been proposed. In چکیده کاملAccurate segmentation of brain tumor plays a key role in the diagnosis of brain tumor. Preset and precise diagnosis of Magnetic Resonance Imaging (MRI) brain tumor is enormously significant for medical analysis. During the last years many methods have been proposed. In this research, a novel fuzzy approach has been proposed to classify a given MRI brain image as normal or cancer label and the intensity of the disease. The applied method first employed feature selection algorithms to extract features from images, and then followed by applying a median filter to reduce the dimensions of features. The brain MRI offers a valuable method to perform pre-and-post surgical evaluations, which are keys to define procedures and to verify their effects. The reduced dimension was submitted to a diagnosis algorithm. We retrospectively investigated a total of 19 treatment plans, each of whom has CT simulation and MRI images acquired during pretreatment. The dose distributions of the same treatment plans were calculated on original CT simulation images as ground truth, as well as on pseudo CT images generated from MRI images. The simulation results demonstrate that the proposed algorithm is promising. پرونده مقاله -
دسترسی آزاد مقاله
4 - Diagnosis of brain tumor using image processing and determination of its type with RVM neural networks
elahe alipoor azar Nasser LotfivandTypically, the diagnosis of a tumor is done through surgical sampling, which is more precise with existing methods. The difference is that this is an aggressive, time consuming and expensive way. In the statistical method, due to the complexity of the brain tissues and چکیده کاملTypically, the diagnosis of a tumor is done through surgical sampling, which is more precise with existing methods. The difference is that this is an aggressive, time consuming and expensive way. In the statistical method, due to the complexity of the brain tissues and the similarity between the cancerous cells and the natural tissues, even a radiologist or an expert physician may also be in error in his diagnosis. Tumor diagnosis is done automatically and various results are achieved. The steps involved in these algorithms can be divided into two sections of the feature discovery and the classification of the samples. The methods generally are that, firstly, the properties of the image are extracted. These characteristics usually include static properties such as entropy, skewness, mean, energy, torque, correlation, etc., or the properties of other algorithms (instant conversion, histogram, etc.). The information obtained at this stage is applied to the sample classification process for decision making. This section is done with an advanced neural network such as RVM. Possible neural networks have the ability to classify more than one class and a kind of radar disease to extract features from MRI images using histogram or satellite conversion techniques, and then selecting appropriate features and ultimately using the system. Fuzzy Neural Network Diagnostics The decision making system of the fuzzy system is a conclusion that trains with these features and in the output, multiple images are given at different levels. In this research, using image and image processing, we try to find out exactly where the brain is placed. For this purpose, it is initially performed using preventive techniques such as enhancement of contrast, marginalization and morphological functions, and then using the neural network to perform a careful separation of the cancerous parts of the brain health sectors. پرونده مقاله -
دسترسی آزاد مقاله
5 - Classification of Brain Tumor Grades by MRI Images using Artificial Neural Network
Melika Aboutalebi Rezvan AbbasiIn recent years, the use of MRI images has been very much considered due to their high clarity and high quality in the diagnosis and determination of brain tumor and its features. In this study, to improve the performance of tumor detection, we investigated comparative چکیده کاملIn recent years, the use of MRI images has been very much considered due to their high clarity and high quality in the diagnosis and determination of brain tumor and its features. In this study, to improve the performance of tumor detection, we investigated comparative approach of the different classifiers to select the most appropriate classifier for identifying and extracting abnormal tissue and selected the best one by comparing their detection accuracies rate. In this research, GLCM and GLRM methods are used to extracting discriminating features. Thus results in they reduce the computational complexity. fuzzy entropy measurement method is used to determine the optimal properties and finally, we compared the four FFNN, MLP, BPNN, ANFIS neural networks to perform the decision making and classification process. The purpose of these four neural networks are to develop tools for discriminating the malignant tumors from benign ones assisting deciding in clinical diagnosis. Based on the results, we achieved high results among all classifiers. The proposed methodology results in accurate and speedy detection of tumor in brain along with identification of precise location of the tumor. In our opinion, the use of these classifiers can be very useful in the diagnosis of brain tumors in MRI images. Our other goal is to prove the suitability of the ANN method as a valuable method for statistical methods. The novelty of the paper lies in the implementation of the proposed method for discriminating the malignant tumors from benign which results in accurate and speedy detection of tumor in brain along with identification of precise location of the tumor. The efficiency of the method is proved through plenty of simulations and comparisons. پرونده مقاله -
دسترسی آزاد مقاله
6 - Brain Tumor Detection Using Deep Transfer Learning Method
Alireza Fazelnia Hassan Masoumi Mohammad Fatehi Jasem JamaliAccurate brain tumor MR images detection plays an important role in diagnosis and treatment decision making. The machine learning methods for classification only uses low-level or high-level features, to tackle the problem of classifications using some handcrafted featu چکیده کاملAccurate brain tumor MR images detection plays an important role in diagnosis and treatment decision making. The machine learning methods for classification only uses low-level or high-level features, to tackle the problem of classifications using some handcrafted features. Development on deep learning, transfer learning and deep convolution neural networks (CNNs) has shown great progress and has succeeded in the image classification task. Deep learning is very powerful for feature representation. In this study, deep transfer learning method for features extraction and detection is used that it does not use any handcrafted features, and needs minimal preprocessing. Transfer learning is a method of transferring information during training and testing. In this study, features extraction from images with pre-trained CNN method, namely, GoogLeNet, VGGNet and AlexNet, for tumor detection is used. The accuracy of tumor detection is 99.84%. The results show that our method, shows the best accuracy for detections tumor پرونده مقاله -
دسترسی آزاد مقاله
7 - Application of firefly algorithm in automatic extraction of brain tumor from multi-modality magnetic resonance images
Hayneh Fathi-Sanghari Neda BehzadfarIn this paper, an automated method for identifying the overall range of the tumor and extracting the starting point of brain tumors in Magnetic Resonance Imaging is presented. In this study, images of patients with glioblastoma multiforme were used. By first combining t چکیده کاملIn this paper, an automated method for identifying the overall range of the tumor and extracting the starting point of brain tumors in Magnetic Resonance Imaging is presented. In this study, images of patients with glioblastoma multiforme were used. By first combining the features of the four MRI modalities, annoying areas such as the eyes, skull, and cerebrospinal fluid that may be problematic are removed. Brain tumors are highly bright in T1-Post images and dark in T1 images. Therefore, calculating the difference between these two images improves the resolution of the tumor area. After performing preprocessing and increasing the resolution of the tumor area, the enclosed frame (BB) algorithm is used. This algorithm is an automatic and fast segmentation method that determines the location of the tumor and its approximate size. After finding the presence of the tumor, the firefly algorithm is used to find the initial point of the tumor. By defining the objective function of moving fireflies to a point that has the maximum light intensity, we can find the point where the probability of a tumor is high. Next, using the growth of the tumor area, the entire tumor area can be extracted. The results show the appropriate speed and accuracy of the proposed method. پرونده مقاله -
دسترسی آزاد مقاله
8 - Diagnosis of Brain Tumor Position in Magnetic Resonance Images by Combining Bounding Box Algorithms, Artificial Bee Colonies and Grow Cut
Mahdi Shafiof Neda BehzadfarTumor detection and isolation in magnetic resonance imaging (MRI) is a significant consideration, but when done manually by people, it is very time consuming and may not be accurate. Also, the appearance of the tumor tissue varies from patient to patient, and there are چکیده کاملTumor detection and isolation in magnetic resonance imaging (MRI) is a significant consideration, but when done manually by people, it is very time consuming and may not be accurate. Also, the appearance of the tumor tissue varies from patient to patient, and there are similarities between the tumor and the natural tissue of the brain. In this paper, we have tried to provide an automated method for diagnosing and displaying brain tumors in MRI images. Images of patients with glioblastoma were used after applying pre-processing and removing areas that have no useful information (such as eyes, scalp, etc.). We used a bounding box algorithm, to create a projection for to determining the initial range of the tumor in the next step, an artificial bee colony algorithm, to determine an initial point of the tumor area and then the Grow cut algorithm for, the exact boundary of the tumor area. Our method is automatic and extensively independent of the operator. comparison between results of 12 patients in our method with other similar methods indicate a high accuracy of the proposed method (about 98%) in comparison s. پرونده مقاله
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