scholarly journals Security Based Brain Tumor Classification using Image Fusion

2020 ◽  
Vol 9 (7) ◽  
pp. 1315-1318
Keyword(s):  
Image Processing ◽  
Brain Tumor ◽  
Image Fusion ◽  
Data Storage ◽  
Efficient Method ◽  
Medical Images ◽  
Tumor Classification

Today, the use of medical images is often complicated for diagnosis process and planning of treatment. The major challenge in image processing and fusion includes data mismatching, data storage issues and security constraints. Although several techniques are being used for image processing, they lack in security parameters. Our objective is to provide an efficient method for image fusion techniques along with the security paradigms. In order to provide security, encryption standards are used. The results of improved framework give better performance and quality over existing methods in terms of security, database information, and fusion factor.

scholarly journals Brain Tumor Classification & Segmentation by Using Advanced DNN, CNN & ResNet-50 Neural Networks

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Brain Tumor ◽  
Image Classification ◽  
Medical Image ◽  
Data Augmentation ◽  
Medical Images ◽  
Medical Science ◽  
Vital Role ◽  
Tumor Classification

In the medical domain, brain image classification is an extremely challenging field. Medical images play a vital role in making the doctor's precise diagnosis and in the surgery process. Adopting intelligent algorithms makes it feasible to detect the lesions of medical images quickly, and it is especially necessary to extract features from medical images. Several studies have integrated multiple algorithms toward medical images domain. Concerning feature extraction from the medical image, a vast amount of data is analyzed to achieve processing results, helping physicians deliver more precise case diagnoses. Image processing mechanism becomes extensive usage in medical science to advance the early detection and treatment aspects. In this aspect, this paper takes tumor, and healthy images as the research object and primarily performs image processing and data augmentation process to feed the dataset to the neural networks. Deep neural networks (DNN), to date, have shown outstanding achievement in classification and segmentation tasks. Carrying this concept into consideration, in this study, we adopted a pre-trained model Resnet_50 for image analysis. The paper proposed three diverse neural networks, particularly DNN, CNN, and ResNet-50. Finally, the splitting dataset is individually assigned to each simplified neural network. Once the image is classified as a tumor accurately, the OTSU segmentation is employed to extract the tumor alone. It can be examined from the experimental outcomes that the ResNet-50 algorithm shows high accuracy 0.996, precision 1.00 with best F1 score 1.0, and minimum test losses of 0.0269 in terms of Brain tumor classification. Extensive experiments prove our offered tumor detection segmentation efficiency and accuracy. To this end, our approach is comprehensive sufficient and only requires minimum pre-and post-processing, which allows its adoption in various medical image classification & segmentation tasks.

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.

scholarly journals A Transfer Learning–Based Active Learning Framework for Brain Tumor Classification

2021 ◽  
Vol 4 ◽  
Author(s):  
Ruqian Hao ◽  
Khashayar Namdar ◽  
Lin Liu ◽  
Farzad Khalvati
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Active Learning ◽  
Transfer Learning ◽  
Medical Images ◽  
Tumor Classification ◽  
Training Data ◽  
Validation Dataset ◽  
Low Grade ◽  
Learning Framework

Brain tumor is one of the leading causes of cancer-related death globally among children and adults. Precise classification of brain tumor grade (low-grade and high-grade glioma) at an early stage plays a key role in successful prognosis and treatment planning. With recent advances in deep learning, artificial intelligence–enabled brain tumor grading systems can assist radiologists in the interpretation of medical images within seconds. The performance of deep learning techniques is, however, highly depended on the size of the annotated dataset. It is extremely challenging to label a large quantity of medical images, given the complexity and volume of medical data. In this work, we propose a novel transfer learning–based active learning framework to reduce the annotation cost while maintaining stability and robustness of the model performance for brain tumor classification. In this retrospective research, we employed a 2D slice–based approach to train and fine-tune our model on the magnetic resonance imaging (MRI) training dataset of 203 patients and a validation dataset of 66 patients which was used as the baseline. With our proposed method, the model achieved area under receiver operating characteristic (ROC) curve (AUC) of 82.89% on a separate test dataset of 66 patients, which was 2.92% higher than the baseline AUC while saving at least 40% of labeling cost. In order to further examine the robustness of our method, we created a balanced dataset, which underwent the same procedure. The model achieved AUC of 82% compared with AUC of 78.48% for the baseline, which reassures the robustness and stability of our proposed transfer learning augmented with active learning framework while significantly reducing the size of training data.

scholarly journals Development of Easyget Algorithm for Deep Learning Sculptor Deepcnet Model using Hadoop Architecture

2020 ◽  
Vol 9 (4) ◽  
pp. 1646-1650
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Data Storage ◽  
Medical Images ◽  
Manual Segmentation ◽  
Storage Space ◽  
Automatic Data ◽  
Storage And Retrieval ◽  
The Brain

Manual segmentation in the brain tumors analyses for malignancy prognosis, via massive amount MRI images produced through medical routine, frustrating task and is a hard. There is a dependence on automated brain tumor graphic segmentation. The amount of precision necessary for scientific purposes is normally as yet not known, and so can't be conveniently quantified actually by means of professional physicians. That is a fascinating point, which includes just sparsely been resolved in the literature, but is nonetheless truly relevant up to now. Additionally, storage space automatization for medical images is essential need nowadays. To carry out very quickly analysis as well as, prognosis there's an imperative want of automated photo storage. Hence, this paper focused on development of new algorithm called “EasyGet” for automatic data storage and retrieval using Hadoop architecture

Real Time Image Fusion Based Technique for Medical Images

2020 ◽  
Vol 17 (9) ◽  
pp. 4500-4508
Author(s):  
H. R. Ramya ◽  
B. K. Sujatha
Keyword(s):  
Image Processing ◽  
Fuzzy Logic ◽  
Image Fusion ◽  
Real Time ◽  
Medical Images ◽  
Computation Time ◽  
Real Time Image ◽  
Fuzzy Logic Technique ◽  
Time Image

To tackle the cost of storage and storage space with fast-growing technologies, the image fusion is playing an important role in several image-processing areas such as medical-imaging and satelliteimaging. This fused picture is appropriate for machine perception, human visual analysis or further analysis assignment. Recently the computing method such as fuzzy logic model has been extensively used in the field of image-processing due to the uniqueness of handling uncertain modeling. The fuzzy logic based image-fusion model generally performed better with respect to other existing image fusion models. In this paper, we considered type-2 fuzzy logic, which has similar function to earlier fuzzy logic technique but consist more functionality that allows optimized management of higher degrees under uncertainty. Interval type-2 fuzzy-logic-system (IT2FLS) are widely used fuzzy sets due to their ease of use and computational simplicity. A real time image fusion (RTIF) technique that is based on the IT2FLS is used to overcome the excess computation time and nonlinear uncertainties, which is present in the medical images. In the result simulation section, we have shown that our proposed model has taken less computation time and provided better quality assessment matrices with respect to existing system.

Algorithms for segmentation and recognition of objects on medical images based on chiarlet transformation and neural networks

2020 ◽  
pp. 35-45
Author(s):  
Y.A. Hamad ◽  
K.V. Simonov ◽  
A.S. Kents
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Computer Vision ◽  
Edge Detection ◽  
Medical Images ◽  
Classification Algorithms ◽  
Visual Data ◽  
Recognition Of Objects

The paper considers general approaches to image processing, analysis of visual data and computer vision. The main methods for detecting features and edges associated with these approaches are presented. A brief description of modern edge detection and classification algorithms suitable for isolating and characterizing the type of pathology in the lungs in medical images is also given.

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