scholarly journals Fuzzy System Based Medical Image Processing for Brain Disease Prediction

2021 ◽  
Vol 15 ◽  
Author(s):  
Mandong Hu ◽  
Yi Zhong ◽  
Shuxuan Xie ◽  
Haibin Lv ◽  
Zhihan Lv
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Fuzzy System ◽  
Medical Image ◽  
Brain Mri ◽  
Medical Image Processing ◽  
Brain Disease ◽  
Simulation Experiments ◽  
Fuzzy C Means ◽  
The Brain

The present work aims to explore the performance of fuzzy system-based medical image processing for predicting the brain disease. The imaging mechanism of NMR (Nuclear Magnetic Resonance) and the complexity of human brain tissues cause the brain MRI (Magnetic Resonance Imaging) images to present varying degrees of noise, weak boundaries, and artifacts. Hence, improvements are made over the fuzzy clustering algorithm. A brain image processing and brain disease diagnosis prediction model is designed based on improved fuzzy clustering and HPU-Net (Hybrid Pyramid U-Net Model for Brain Tumor Segmentation) to ensure the model safety performance. Brain MRI images collected from a Hospital, are employed in simulation experiments to validate the performance of the proposed algorithm. Moreover, CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), FCM (Fuzzy C-Means), LDCFCM (Local Density Clustering Fuzzy C-Means), and AFCM (Adaptive Fuzzy C-Means) are included in simulation experiments for performance comparison. Results demonstrate that the proposed algorithm has more nodes, lower energy consumption, and more stable changes than other models under the same conditions. Regarding the overall network performance, the proposed algorithm can complete the data transmission tasks the fastest, basically maintaining at about 4.5 s on average, which performs remarkably better than other models. A further prediction performance analysis reveals that the proposed algorithm provides the highest prediction accuracy for the Whole Tumor under DSC (Dice Similarity Coefficient), reaching 0.936. Besides, its Jaccard coefficient is 0.845, proving its superior segmentation accuracy over other models. In a word, the proposed algorithm can provide higher accuracy, a more apparent denoising effect, and the best segmentation and recognition effect than other models while ensuring energy consumption. The results can provide an experimental basis for the feature recognition and predictive diagnosis of brain images.

Brain Tumor Segmentation Based on Region of Interest-Aided Localization and Segmentation U-Net

2021 ◽  
Author(s):  
Shidong Li ◽  
Jianwei Liu ◽  
Zhanjie Song
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Brain Tumor ◽  
Medical Image ◽  
Brain Mri ◽  
Medical Image Processing ◽  
Tumor Segmentation ◽  
Dice Similarity Coefficient ◽  
Brain Tumor Segmentation ◽  
The Impact

Abstract Since magnetic resonance imaging (MRI) has superior soft tissue contrast, contouring (brain) tumor accurately by MRI images is essential in medical image processing. Segmenting tumor accurately is immensely challenging, since tumor and normal tissues are often inextricably intertwined in the brain. It is also extremely time consuming manually. Late deep learning techniques start to show reasonable success in brain tumor segmentation automatically. The purpose of this study is to develop a new region-ofinterest-aided (ROI-aided) deep learning technique for automatic brain tumor MRI segmentation. The method consists of two major steps. Step one is to use a 2D network with U-Net architecture to localize the tumor ROI, which is to reduce the impact of normal tissue’s disturbance. Then a 3D U-Net is performed in step 2 for tumor segmentation within identified ROI. The proposed method is validated on MICCAI BraTS 2015 Challenge with 220 high Gliomas grade (HGG) and 54 low Gliomas grade (LGG) patients’ data. The Dice similarity coefficient and the Hausdorff distance between the manual tumor contour and that segmented by the proposed method are 0.876 ±0.068 and 3.594±1.347 mm, respectively. These numbers are indications that our proposed method is an effective ROI-aided deep learning strategy for brain MRI tumor segmentation, and a valid and useful tool in medical image processing.

scholarly journals AK-DL: A Shallow Neural Network Model for Diagnosing Actinic Keratosis with Better Performance than Deep Neural Networks

Diagnostics ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 217
Author(s):  
Liyang Wang ◽  
Angxuan Chen ◽  
Yan Zhang ◽  
Xiaoya Wang ◽  
Yu Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Actinic Keratosis ◽  
Medical Image ◽  
Histopathological Examination ◽  
Diagnostic System ◽  
Medical Image Processing ◽  
Deep Cnn ◽  
Image Processing Algorithms ◽  
Processing Algorithms

Actinic keratosis (AK) is one of the most common precancerous skin lesions, which is easily confused with benign keratosis (BK). At present, the diagnosis of AK mainly depends on histopathological examination, and ignorance can easily occur in the early stage, thus missing the opportunity for treatment. In this study, we designed a shallow convolutional neural network (CNN) named actinic keratosis deep learning (AK-DL) and further developed an intelligent diagnostic system for AK based on the iOS platform. After data preprocessing, the AK-DL model was trained and tested with AK and BK images from dataset HAM10000. We further compared it with mainstream deep CNN models, such as AlexNet, GoogLeNet, and ResNet, as well as traditional medical image processing algorithms. Our results showed that the performance of AK-DL was better than the mainstream deep CNN models and traditional medical image processing algorithms based on the AK dataset. The recognition accuracy of AK-DL was 0.925, the area under the receiver operating characteristic curve (AUC) was 0.887, and the training time was only 123.0 s. An iOS app of intelligent diagnostic system was developed based on the AK-DL model for accurate and automatic diagnosis of AK. Our results indicate that it is better to employ a shallow CNN in the recognition of AK.

Recent Advances in Medical Image Processing

2020 ◽  
pp. 1-14
Author(s):  
Zhen Huang ◽  
Qiang Li ◽  
Ju Lu ◽  
Junlin Feng ◽  
Jiajia Hu ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Image Processing ◽  
Medical Imaging ◽  
Medical Image ◽  
Medical Image Processing ◽  
Convolution Neural Network ◽  
Artificial Intelligence Technology ◽  
Deep Convolution Neural Network

<b><i>Background:</i></b> Application and development of the artificial intelligence technology have generated a profound impact in the field of medical imaging. It helps medical personnel to make an early and more accurate diagnosis. Recently, the deep convolution neural network is emerging as a principal machine learning method in computer vision and has received significant attention in medical imaging. <b><i>Key Message:</i></b> In this paper, we will review recent advances in artificial intelligence, machine learning, and deep convolution neural network, focusing on their applications in medical image processing. To illustrate with a concrete example, we discuss in detail the architecture of a convolution neural network through visualization to help understand its internal working mechanism. <b><i>Summary:</i></b> This review discusses several open questions, current trends, and critical challenges faced by medical image processing and artificial intelligence technology.

scholarly journals Digital Medical Image Segmentation Using Fuzzy C-Means Clustering

2020 ◽  
Vol 4 (1) ◽  
pp. 51 ◽  
Author(s):  
Bakhtyar Ahmed Mohammed ◽  
Muzhir Shaban Al-Ani
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Brain Mri ◽  
Accurate Method ◽  
Medical Image Segmentation ◽  
Fuzzy C Means ◽  
Fcm Clustering ◽  
The Brain ◽  
Digital Medical Image

In the modern globe, digital medical image processing is a major branch to study in the fields of medical and information technology. Every medical field relies on digital medical imaging in diagnosis for most of their cases. One of the major components of medical image analysis is medical image segmentation. Medical image segmentation participates in the diagnosis process, and it aids the processes of other medical image components to increase the accuracy. In unsupervised methods, fuzzy c-means (FCM) clustering is the most accurate method for image segmentation, and it can be smooth and bear desirable outcomes. The intention of this study is to establish a strong systematic way to segment complicate medical image cases depend on the proposed method to share in the decision-making process. This study mentions medical image modalities and illustrates the steps of the FCM clustering method mathematically with example. It segments magnetic resonance imaging (MRI) of the brain to separate tumor inside the brain MRI according to four statuses.

scholarly journals Impact of Upstream Medical Image Processing on the Downstream Performance of a Head CT Triage Neural Network

2021 ◽  
pp. e200229
Author(s):  
Sarah M. Hooper ◽  
Jared A. Dunnmon ◽  
Matthew P. Lungren ◽  
Domenico Mastrodicasa ◽  
Daniel L. Rubin ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Medical Image ◽  
Medical Image Processing ◽  
Head Ct

scholarly journals Hybrid Manta Ray Foraging Optimization for Novel Brain Tumor Detection

2020 ◽  
Vol 2 (3) ◽  
pp. 175-185
Author(s):  
Karrupusamy P.
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Image Processing ◽  
Feature Selection ◽  
Brain Tumor ◽  
Medical Image ◽  
Early Stage ◽  
Medical Image Processing ◽  
Human Effort ◽  
Manta Ray

In medical image processing, segmentation and extraction of tumor portion from brain MRI is a complex task. It consumes more time and human effort to differentiate the normal and abnormal tissue. Clinical experts need more time to provide accurate results, recent technology developments in image processing reduces the human effort and provides more accurate results which reduces time and death rates by identifying the issues in early stage itself. Machine learning based algorithms occupies a major role in bio medical image processing applications. The performance of machine learning models is in satisfactory levels, but it could be improved by introducing optimization in feature selection stage itself. The research work provides a hybrid manta ray foraging optimization for feature selection from brain tumor MRI images. Convolution neural network is used to test the optimized features and detects the early stage brain tumors. The experimental model is compared with existing artificial neural network, particle swarm optimization algorithm and acquires a better detection and classification accuracy.

scholarly journals Application of CNN Algorithm Based on Chaotic Recursive Diagonal Model in Medical Image Processing

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Fangfang Ye ◽  
Sen Xu ◽  
Ting Wang ◽  
Zhangquan Wang ◽  
Tiaojuan Ren
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Medical Image ◽  
Medical Science ◽  
Medical Image Processing ◽  
Living Standards ◽  
Disease Rate ◽  
Application Research ◽  
Paper Briefly ◽  
Gradual Improvement

With the gradual improvement of people’s living standards, the production and drinking of all kinds of food is increasing. People’s disease rate has increased compared with before, which leads to the increasing number of medical image processing. Traditional technology cannot meet most of the needs of medicine. At present, convolutional neural network (CNN) algorithm using chaotic recursive diagonal model has great advantages in medical image processing and has become an indispensable part of most hospitals. This paper briefly introduces the use of medical science and technology in recent years. The hybrid algorithm of CNN in chaotic recursive diagonal model is mainly used for technical research, and the application of this technology in medical image processing is analysed. The CNN algorithm is optimized by using chaotic recursive diagonal model. The results show that the chaotic recursive diagonal model can improve the structure of traditional neural network and improve the efficiency and accuracy of the original CNN algorithm. Then, the application research and comparison of medical image processing are performed according to CNN algorithm and optimized CNN algorithm. The experimental results show that the CNN algorithm optimized by chaotic recursive diagonal model can help medical image automatic processing and patient condition analysis.

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