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.

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.

Bio-Medical Image Processing

2020 ◽  
pp. 158-169
Author(s):  
Rasmita Lenka ◽  
Koustav Dutta ◽  
Ashimananda Khandual ◽  
Soumya Ranjan Nayak
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Processing ◽  
Deep Learning ◽  
Early Detection ◽  
Digital Image Processing ◽  
Medical Science ◽  
Medical Image Processing ◽  
The World ◽  
Medical Reports

The chapter focuses on application of digital image processing and deep learning for analyzing the occurrence of malaria from the medical reports. This approach is helpful in quick identification of the disease from the preliminary tests which are carried out in a person affected by malaria. The combination of deep learning has made the process much advanced as the convolutional neural network is able to gain deeper insights from the medical images of the person. Since traditional methods are not able to detect malaria properly and quickly, by means of convolutional neural networks, the early detection of malaria has been possible, and thus, this process will open a new door in the world of medical science.

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 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.

Special Issue on Information Medical Science

2012 ◽  
Vol 16 (1) ◽  
pp. 25-25
Author(s):  
Yutaka Hatakeyama
Keyword(s):  
Image Processing ◽  
Medical Image ◽  
Social Impact ◽  
Medical Science ◽  
Medical Image Processing ◽  
Medical Data ◽  
Navigation Systems ◽  
Special Issue ◽  
Hospital Data ◽  
Analytical Approaches

One important and interesting application in computational intelligence technology is medical application. Medical engineering research covers such areas as medical image processing. With most health information now described electronically, hospitals are accumulating large amounts of medical data, including imaging, text, and structured materials. This has made ginformation medical scienceh vastly more important in improving healthcare technology. This special issue introduces an application based on hospital data, medical care support systems, and medical image processing. The application deals with analytical approaches, navigation systems, medical data management, and text mining for summary data. The support system shows diagnosis and robotassisted therapy. Medical imaging targets X-ray, blood cell, and ultrasonography images. As guest editors, we can assure the readers that the papers in this special issue have great social impact in this research area and encourage good relations with engineering and medical practice approaches. We thank the contributors and reviewers for introduce these latest achievements in this exciting field.

Comparative Analysis of Deep Convolutional Neural Network Models for Humerus Bone Fracture Detection

2021 ◽  
Vol 11 (12) ◽  
pp. 3117-3122
Author(s):  
A. Sasidhar ◽  
M. S. Thanabal
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Bone Fracture ◽  
Medical Image ◽  
Bone Fractures ◽  
Medical Image Processing ◽  
Fracture Detection ◽  
Wide Range

Deep learning plays a key role in medical image processing. One of the applications of deep learning models in this domain is bone fracture detection from X-ray images. Convolutional neural network and its variants are used in wide range of medical image processing applications. MURA Dataset is commonly used in various studies that detect bone fractures and this work also uses that dataset, in specific the Humerus bone radiograph images. The humerus dataset in the MURA dataset contains both images with fracture and without fracture. The image with fracture includes images with metals which are removed in this work. Experimental analysis was made with two variants of convolutional neural network, DenseNet169 Model and the VGG Model. In case of the DenseNet169 model, a model with the pre trained weights of ImageNet and one without it is experimented. Results obtained with these variants of CNN are comparedand it shows that DenseNet169 model that uses pre-trained weights of ImageNet model performs better than the other two models.

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