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 Hybrid Manta Ray Foraging Optimization for Novel Brain Tumor Detection

2020 ◽  
Vol 2 (3) ◽  
pp. 175-185
Author(s):  
Dr. P. Karrupusamy
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.

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.

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.

Cotton Leaf Disease Detection by Feature Extraction

2016 ◽  
pp. 89-104
Author(s):  
Savita N. Ghaiwat ◽  
Parul Arora
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Image Processing ◽  
Feature Extraction ◽  
Early Stage ◽  
Cotton Leaf ◽  
Control Methods ◽  
The Neural Network ◽  
The World ◽  
Leaf Image

Cotton leaf diseases have occurred all over the world, including India. They adversely affect cotton quality and yield. Technology can help in identifying disease in early stage so that effective treatment can be given immediately. Now, the control methods rely mainly on artificial means. This paper propose application of image processing and machine learning in identifying three cotton leaf diseases through feature extraction. Using image processing, 12 types of features are extracted from cotton leaf image then the pattern was learned using BP Neural Network method in machine learning process. Three diseases have been diagnosed, namely Powdery mildew, Downy mildew and leafminer. The Neural Network classification performs well and could successfully detect and classify the tested disease.

Deep Learning Applications in Medical Imaging

2021 ◽  
pp. 156-177
Author(s):  
Janani Viswanathan ◽  
N. Saranya ◽  
Abinaya Inbamani
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
Deep Learning ◽  
Medical Image ◽  
Medical Image Processing ◽  
Medical Diagnostics ◽  
Healthcare Sector ◽  
Computational Techniques ◽  
Healthcare Applications ◽  
Medical Sector

Deep learning (DL) and artificial intelligence (AI) are emerging tools in the healthcare sector for medical diagnostics. This chapter elaborates on general reasons for the popularity of computational techniques such as deep learning and machine learning (ML) applications in the medical image processing domain. The initial part of this chapter focuses on reviewing the fundamental concepts of DL algorithms, competence with machine learning, need in healthcare, applications, and challenges in medical image processing. Doing so allows understanding the reasons for the construction of all of them and offers a different view on various domains in the medical sector. The tools and technology required for DL, selection, implementation, optimization, and testing are discussed with respect to an application of cancer detection. Thus, this chapter gives an overall vision of deep learning concepts related to biomedical research.

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