Pub Date : 1900-01-01DOI: 10.4018/978-1-7998-8929-8.ch008
S. Saeed, A. Abdullah, N. Jhanjhi, M. Naqvi, Muneer Ahmad
Brain metastases are the most prevalent intracranial neoplasm that causes excessive morbidity and mortality in most cancer patients. The current medical model for brain metastases is focused on the physical condition of the affected individual, the anatomy of the main tumor, and the number and proximity of brain lesions. In this paper, a new hybrid Metastases Fast Fourier Transformation with SVM (MFFT-SVM) method is proposed that can classify high dimensional magnetic resonance imaging as tumor and predicts lung cancer from given protein primary sequences. The goal is to address the associated issues stated with the treatment targeted at unique molecular pathways to the tumor, together with those involved in crossing the blood-brain barrier and migrating cells to the lungs. The proposed method identifies the place of the lung damage by the Fast Fourier Technique (FFT). FFT is the principal statistical approach for frequency analysis which has many engineering and scientific uses. Moreover, Differential Fourier Transformation (DFT) is considered for focusing the brain metastases that migrate into the lungs and create non-small lungs cancer. However, Support Vector Machine (SVM) is used to measure the accuracy of control patient's datasets of sensitivity and specificity. The simulation results verified the performance of the proposed method is improved by 92.8% sensitivity, of 93.2% specificity and 95.5% accuracy respectively.
{"title":"Optimized Hybrid Prediction Method for Lung Metastases","authors":"S. Saeed, A. Abdullah, N. Jhanjhi, M. Naqvi, Muneer Ahmad","doi":"10.4018/978-1-7998-8929-8.ch008","DOIUrl":"https://doi.org/10.4018/978-1-7998-8929-8.ch008","url":null,"abstract":"Brain metastases are the most prevalent intracranial neoplasm that causes excessive morbidity and mortality in most cancer patients. The current medical model for brain metastases is focused on the physical condition of the affected individual, the anatomy of the main tumor, and the number and proximity of brain lesions. In this paper, a new hybrid Metastases Fast Fourier Transformation with SVM (MFFT-SVM) method is proposed that can classify high dimensional magnetic resonance imaging as tumor and predicts lung cancer from given protein primary sequences. The goal is to address the associated issues stated with the treatment targeted at unique molecular pathways to the tumor, together with those involved in crossing the blood-brain barrier and migrating cells to the lungs. The proposed method identifies the place of the lung damage by the Fast Fourier Technique (FFT). FFT is the principal statistical approach for frequency analysis which has many engineering and scientific uses. Moreover, Differential Fourier Transformation (DFT) is considered for focusing the brain metastases that migrate into the lungs and create non-small lungs cancer. However, Support Vector Machine (SVM) is used to measure the accuracy of control patient's datasets of sensitivity and specificity. The simulation results verified the performance of the proposed method is improved by 92.8% sensitivity, of 93.2% specificity and 95.5% accuracy respectively.","PeriodicalId":148158,"journal":{"name":"Approaches and Applications of Deep Learning in Virtual Medical Care","volume":"2013 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127434598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-7998-8929-8.ch006
Khalid A. Al Afandy, Hicham Omara, M. Lazaar, Mohammed Al Achhab
This chapter provides a comprehensive explanation of deep learning including an introduction to ANNs, improving the deep NNs, CNNs, classic networks, and some technical tricks for image classification using deep learning. ANNs, mathematical models for one node ANN, and multi-layers/multi-nodes ANNs are explained followed by the ANNs training algorithm followed by the loss function, the cost function, the activation function with its derivatives, and the back-propagation algorithm. This chapter also outlines the most common training problems with the most common solutions and ANNs improvements. CNNs are explained in this chapter with the convolution filters, pooling filters, stride, padding, and the CNNs mathematical models. This chapter explains the four most commonly used classic networks and ends with some technical tricks that can be used in CNNs model training.
{"title":"Deep Learning","authors":"Khalid A. Al Afandy, Hicham Omara, M. Lazaar, Mohammed Al Achhab","doi":"10.4018/978-1-7998-8929-8.ch006","DOIUrl":"https://doi.org/10.4018/978-1-7998-8929-8.ch006","url":null,"abstract":"This chapter provides a comprehensive explanation of deep learning including an introduction to ANNs, improving the deep NNs, CNNs, classic networks, and some technical tricks for image classification using deep learning. ANNs, mathematical models for one node ANN, and multi-layers/multi-nodes ANNs are explained followed by the ANNs training algorithm followed by the loss function, the cost function, the activation function with its derivatives, and the back-propagation algorithm. This chapter also outlines the most common training problems with the most common solutions and ANNs improvements. CNNs are explained in this chapter with the convolution filters, pooling filters, stride, padding, and the CNNs mathematical models. This chapter explains the four most commonly used classic networks and ends with some technical tricks that can be used in CNNs model training.","PeriodicalId":148158,"journal":{"name":"Approaches and Applications of Deep Learning in Virtual Medical Care","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132134139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-7998-8929-8.ch003
Smita Das, Swanirbhar Majumder
Diabetic retinopathy (DR) detection techniques is a biometric modality that deserves systematic review and analysis of the connected algorithms for further improvement. The ophthalmologist uses retinal fundus images for the early detection of DR by segmenting the images. There are several segmentation algorithms reported as earlier. This chapter presents a comprehensive review of the methodology associated with retinal blood vessel extraction presented to date. The vessel segmentation techniques are divided into four main categories depending on their underlying methodology as pattern recognition, vessel tracking, model based, and hybrid approaches. A few of these methods are further classified into subsections. Finally, a comparative analysis of a few of the DR detection techniques will be presented here based on their merits, demerits, and other parameters like sensitivity, specificity, and accuracy and provide detailed information about its significance, present status, limitations, and future scope.
{"title":"Overview and Analysis of Present-Day Diabetic Retinopathy (DR) Detection Techniques","authors":"Smita Das, Swanirbhar Majumder","doi":"10.4018/978-1-7998-8929-8.ch003","DOIUrl":"https://doi.org/10.4018/978-1-7998-8929-8.ch003","url":null,"abstract":"Diabetic retinopathy (DR) detection techniques is a biometric modality that deserves systematic review and analysis of the connected algorithms for further improvement. The ophthalmologist uses retinal fundus images for the early detection of DR by segmenting the images. There are several segmentation algorithms reported as earlier. This chapter presents a comprehensive review of the methodology associated with retinal blood vessel extraction presented to date. The vessel segmentation techniques are divided into four main categories depending on their underlying methodology as pattern recognition, vessel tracking, model based, and hybrid approaches. A few of these methods are further classified into subsections. Finally, a comparative analysis of a few of the DR detection techniques will be presented here based on their merits, demerits, and other parameters like sensitivity, specificity, and accuracy and provide detailed information about its significance, present status, limitations, and future scope.","PeriodicalId":148158,"journal":{"name":"Approaches and Applications of Deep Learning in Virtual Medical Care","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121656671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this chapter, a deep study of dysgraphia and its various available technical aids is discussed. A person suffering from dysgraphia struggles to carry out day-to-day activities like schoolwork, paperwork, and other writing activities. A suitable aid is required to overcome the hurdles due to the suffering. This literature establishes the various effects of dysgraphia in adults and children. An analysis of various effective tools is carried out in the study. Some tools are directly designed to tackle the inconveniences that come along with this disability; others provide a more general aid for writing. The literature also identifies the patterns and quirks commonly found in the handwriting. Algorithms for handwriting recognition is discussed to lay the foundation of aids present for dysgraphia. The objective of the chapter is to provide foundation work to create aids for dysgraphia by categorizing the various related key points.
{"title":"Virtual Technical Aids to Help People With Dysgraphia","authors":"Navirah Kamal, Pragati Sharma, Rangana Das, Vipul Goyal, Richa Gupta","doi":"10.4018/978-1-7998-8929-8.ch009","DOIUrl":"https://doi.org/10.4018/978-1-7998-8929-8.ch009","url":null,"abstract":"In this chapter, a deep study of dysgraphia and its various available technical aids is discussed. A person suffering from dysgraphia struggles to carry out day-to-day activities like schoolwork, paperwork, and other writing activities. A suitable aid is required to overcome the hurdles due to the suffering. This literature establishes the various effects of dysgraphia in adults and children. An analysis of various effective tools is carried out in the study. Some tools are directly designed to tackle the inconveniences that come along with this disability; others provide a more general aid for writing. The literature also identifies the patterns and quirks commonly found in the handwriting. Algorithms for handwriting recognition is discussed to lay the foundation of aids present for dysgraphia. The objective of the chapter is to provide foundation work to create aids for dysgraphia by categorizing the various related key points.","PeriodicalId":148158,"journal":{"name":"Approaches and Applications of Deep Learning in Virtual Medical Care","volume":"455 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115959015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-7998-8929-8.ch005
R. Rastogi, S. Sagar, N. Tandon, B. Singh, T. Rajeshwari
The happiness programs and seeking their various means are popular across the globe. Many cultures and races are using them in different ways through carnivals, festivals, and occasions. In India, the Yajna, Mantra, Pranayama, and Yoga-like alternate therapies are now drawing attention of researchers, socio behavioral scientists, and philosophers by their scientific divinity. The chapter is an honest effort to identify the logical progress on happiness indices and reduction in radiation of electronic gadgets. The visualizations propound evidence that the ancient Vedic rituals and activities were effective in maintaining the mental balance. The data set was collected after a specified protocol followed and analyzed through various scientific data analysis tools.
{"title":"Computational Statistics on Stress Patients With Happiness and Radiation Indices by Vedic Homa Therapy","authors":"R. Rastogi, S. Sagar, N. Tandon, B. Singh, T. Rajeshwari","doi":"10.4018/978-1-7998-8929-8.ch005","DOIUrl":"https://doi.org/10.4018/978-1-7998-8929-8.ch005","url":null,"abstract":"The happiness programs and seeking their various means are popular across the globe. Many cultures and races are using them in different ways through carnivals, festivals, and occasions. In India, the Yajna, Mantra, Pranayama, and Yoga-like alternate therapies are now drawing attention of researchers, socio behavioral scientists, and philosophers by their scientific divinity. The chapter is an honest effort to identify the logical progress on happiness indices and reduction in radiation of electronic gadgets. The visualizations propound evidence that the ancient Vedic rituals and activities were effective in maintaining the mental balance. The data set was collected after a specified protocol followed and analyzed through various scientific data analysis tools.","PeriodicalId":148158,"journal":{"name":"Approaches and Applications of Deep Learning in Virtual Medical Care","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133015535","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-7998-8929-8.ch002
S. Saeed, N. Jhanjhi, M. Naqvi, Mamoona Humyun, Muneer Ahmad, Loveleen Gaur
Breast cancer is the most common cancer in women aged 59 to 69 years old. Studies have shown that early detection and treatment of breast cancer increases the chances of survival significantly. They also demonstrated that detecting small lesions early improves forecasting and results in a significant reduction in death cases. The most effective screening diagnostic technique in this case is mammography. However, interpretation of mammograms is difficult due to small differences in tissue densities within mammographic images. This is especially true for dense breasts, and this study suggests that screening mammography is more effective in fatty breast tissue than in dense breast tissue. This study focuses on breast cancer diagnosis as well as identifying risk factors and their assessments of breast cancer as well as premature detection of breast cancer by analyzing 3D MRI mammography methods and segmentation of mammographic images using machine learning.
{"title":"Optimized Breast Cancer Premature Detection Method With Computational Segmentation","authors":"S. Saeed, N. Jhanjhi, M. Naqvi, Mamoona Humyun, Muneer Ahmad, Loveleen Gaur","doi":"10.4018/978-1-7998-8929-8.ch002","DOIUrl":"https://doi.org/10.4018/978-1-7998-8929-8.ch002","url":null,"abstract":"Breast cancer is the most common cancer in women aged 59 to 69 years old. Studies have shown that early detection and treatment of breast cancer increases the chances of survival significantly. They also demonstrated that detecting small lesions early improves forecasting and results in a significant reduction in death cases. The most effective screening diagnostic technique in this case is mammography. However, interpretation of mammograms is difficult due to small differences in tissue densities within mammographic images. This is especially true for dense breasts, and this study suggests that screening mammography is more effective in fatty breast tissue than in dense breast tissue. This study focuses on breast cancer diagnosis as well as identifying risk factors and their assessments of breast cancer as well as premature detection of breast cancer by analyzing 3D MRI mammography methods and segmentation of mammographic images using machine learning.","PeriodicalId":148158,"journal":{"name":"Approaches and Applications of Deep Learning in Virtual Medical Care","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122975123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-7998-8929-8.ch004
G. Sharma
Over the past few decades, chronic illnesses have been on a continuous rise of which epilepsy has been the most common neurological disorder. However, due to the recent progress that has been made by medical science, epilepsy can be controlled for about 70% of the cases. To diagnose epilepsy, EEG, CT scan, MRI, etc. are some of the most common ways, but in this chapter, diagnosis using EEG shall be most focused upon. Although EEG can be considered a good way to decide upon the results of epilepsy proving whether a person is epileptic or not, it is not a completely reliable method. Hence, for its accurate detection we must use sophisticated techniques like CNN and LSTM that will provide a timely and correct diagnosis, reducing the chances of frequent epileptic seizures and SUDEP. Using anti-epileptic drugs cannot guarantee epilepsy prevention, and even if they do, these drugs come with some serious side effects, so people must look back to yoga for a probable permanent treatment.
{"title":"Application of Deep Learning in Epilepsy","authors":"G. Sharma","doi":"10.4018/978-1-7998-8929-8.ch004","DOIUrl":"https://doi.org/10.4018/978-1-7998-8929-8.ch004","url":null,"abstract":"Over the past few decades, chronic illnesses have been on a continuous rise of which epilepsy has been the most common neurological disorder. However, due to the recent progress that has been made by medical science, epilepsy can be controlled for about 70% of the cases. To diagnose epilepsy, EEG, CT scan, MRI, etc. are some of the most common ways, but in this chapter, diagnosis using EEG shall be most focused upon. Although EEG can be considered a good way to decide upon the results of epilepsy proving whether a person is epileptic or not, it is not a completely reliable method. Hence, for its accurate detection we must use sophisticated techniques like CNN and LSTM that will provide a timely and correct diagnosis, reducing the chances of frequent epileptic seizures and SUDEP. Using anti-epileptic drugs cannot guarantee epilepsy prevention, and even if they do, these drugs come with some serious side effects, so people must look back to yoga for a probable permanent treatment.","PeriodicalId":148158,"journal":{"name":"Approaches and Applications of Deep Learning in Virtual Medical Care","volume":"110 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131702775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-7998-8929-8.ch001
Preeti Sharma, Devershi Pallavi Bhatt
Medical imaging applications like MRI, CT scan, x-ray, PET, ultrasound, etc. provide health experts fast and comprehensive information of the internal organs and tissues of the human body. MRI of the brain is used to get inside information of any sort of brain injury, tumor, stroke, or wound in a blood vessel. The complex structure of the brain makes it a challenging responsibility for the researcher to design a model to precisely segment the brain region from the skull and to find any abnormality in the tissue. This chapter helps to understand the importance of deep learning to perform segmentation on MRI (magnetic resonance imaging) scans of the brain by reviewing previous studies and also presents brief knowledge of different brain imaging techniques, digital image segmentation techniques, and deep learning.
{"title":"Importance of Deep Learning Models in the Medical Imaging Field","authors":"Preeti Sharma, Devershi Pallavi Bhatt","doi":"10.4018/978-1-7998-8929-8.ch001","DOIUrl":"https://doi.org/10.4018/978-1-7998-8929-8.ch001","url":null,"abstract":"Medical imaging applications like MRI, CT scan, x-ray, PET, ultrasound, etc. provide health experts fast and comprehensive information of the internal organs and tissues of the human body. MRI of the brain is used to get inside information of any sort of brain injury, tumor, stroke, or wound in a blood vessel. The complex structure of the brain makes it a challenging responsibility for the researcher to design a model to precisely segment the brain region from the skull and to find any abnormality in the tissue. This chapter helps to understand the importance of deep learning to perform segmentation on MRI (magnetic resonance imaging) scans of the brain by reviewing previous studies and also presents brief knowledge of different brain imaging techniques, digital image segmentation techniques, and deep learning.","PeriodicalId":148158,"journal":{"name":"Approaches and Applications of Deep Learning in Virtual Medical Care","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124393773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-7998-8929-8.ch010
S. Saeed, Habibullah Bin Haroon, M. Naqvi, N. Jhanjhi, Muneer Ahmad, Loveleen Gaur
Low-grade tumor or CSF fluid, the symptoms of brain tumor and CSF liquid, usually require image segmentation to evaluate tumor detection in brain images. This research uses systematic literature review (SLR) process for analysis of the different segmentation approach for detecting the low-grade tumor and CSF fluid presence in the brain. This research work investigated how to evaluate and detect the tumor and CSF fluid, improve segmentation method to detect tumor through graph cut hidden markov model of k-mean clustering algorithm (GCHMkC) techniques and parameters, extract the missing values in k-NN algorithm through correlation matrix of hybrid k-NN algorithm with time lag and discrete fourier transformation (DFT) techniques and parameters, and convert the non-linear data into linear transformation using LE-LPP and time complexity techniques and parameters.
{"title":"A Systematic Mapping Study of Low-Grade Tumor of Brain Cancer and CSF Fluid Detecting Approaches and Parameters","authors":"S. Saeed, Habibullah Bin Haroon, M. Naqvi, N. Jhanjhi, Muneer Ahmad, Loveleen Gaur","doi":"10.4018/978-1-7998-8929-8.ch010","DOIUrl":"https://doi.org/10.4018/978-1-7998-8929-8.ch010","url":null,"abstract":"Low-grade tumor or CSF fluid, the symptoms of brain tumor and CSF liquid, usually require image segmentation to evaluate tumor detection in brain images. This research uses systematic literature review (SLR) process for analysis of the different segmentation approach for detecting the low-grade tumor and CSF fluid presence in the brain. This research work investigated how to evaluate and detect the tumor and CSF fluid, improve segmentation method to detect tumor through graph cut hidden markov model of k-mean clustering algorithm (GCHMkC) techniques and parameters, extract the missing values in k-NN algorithm through correlation matrix of hybrid k-NN algorithm with time lag and discrete fourier transformation (DFT) techniques and parameters, and convert the non-linear data into linear transformation using LE-LPP and time complexity techniques and parameters.","PeriodicalId":148158,"journal":{"name":"Approaches and Applications of Deep Learning in Virtual Medical Care","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125708554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.4018/978-1-7998-8929-8.ch007
S. Saeed, Habibullah Bin Haroon, N. Jhanjhi, M. Naqvi, Muneer Ahmad
Low-grade tumor or CSF fluid, the symptoms of brain tumour and CSF liquid, usually requires image segmentation to evaluate tumour detection in brain images. This research uses systematic literature review (SLR) process for analysis of the different segmentation approach for detecting the low-grade tumor and CSF fluid presence in the brain. This research work investigated how to evaluate and detect the tumor and CSF fluid, supervised machine learning algorithm and segmentation method (3D and 4D segmentation process, supervised segmentation process, Fourier transformation, and Laplace transformation), and mentioned the details of publication selection with the publishing digital libraries bodies. Furthermore, this research discusses selected segmentation techniques to detect the low-grade tumor and CSF fluid in systematic mapping through systematic literature review (SLR) process.
{"title":"A Systematic Mapping Study of Low-Grade Tumor of Brain Cancer and CSF Fluid Detecting in MRI Images Through Multi-Algorithm Techniques","authors":"S. Saeed, Habibullah Bin Haroon, N. Jhanjhi, M. Naqvi, Muneer Ahmad","doi":"10.4018/978-1-7998-8929-8.ch007","DOIUrl":"https://doi.org/10.4018/978-1-7998-8929-8.ch007","url":null,"abstract":"Low-grade tumor or CSF fluid, the symptoms of brain tumour and CSF liquid, usually requires image segmentation to evaluate tumour detection in brain images. This research uses systematic literature review (SLR) process for analysis of the different segmentation approach for detecting the low-grade tumor and CSF fluid presence in the brain. This research work investigated how to evaluate and detect the tumor and CSF fluid, supervised machine learning algorithm and segmentation method (3D and 4D segmentation process, supervised segmentation process, Fourier transformation, and Laplace transformation), and mentioned the details of publication selection with the publishing digital libraries bodies. Furthermore, this research discusses selected segmentation techniques to detect the low-grade tumor and CSF fluid in systematic mapping through systematic literature review (SLR) process.","PeriodicalId":148158,"journal":{"name":"Approaches and Applications of Deep Learning in Virtual Medical Care","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129957580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：