Big data analytics for preventive medicine
- Razzak, Muhammad, Imran, Muhammad, Xu, Guandong
- Authors: Razzak, Muhammad , Imran, Muhammad , Xu, Guandong
- Date: 2020
- Type: Text , Journal article
- Relation: Neural Computing and Applications Vol. 32, no. 9 (2020), p. 4417-4451
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- Reviewed:
- Description: Medical data is one of the most rewarding and yet most complicated data to analyze. How can healthcare providers use modern data analytics tools and technologies to analyze and create value from complex data? Data analytics, with its promise to efficiently discover valuable pattern by analyzing large amount of unstructured, heterogeneous, non-standard and incomplete healthcare data. It does not only forecast but also helps in decision making and is increasingly noticed as breakthrough in ongoing advancement with the goal is to improve the quality of patient care and reduces the healthcare cost. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of data analytics methods for disease prevention. This review first introduces disease prevention and its challenges followed by traditional prevention methodologies. We summarize state-of-the-art data analytics algorithms used for classification of disease, clustering (unusually high incidence of a particular disease), anomalies detection (detection of disease) and association as well as their respective advantages, drawbacks and guidelines for selection of specific model followed by discussion on recent development and successful application of disease prevention methods. The article concludes with open research challenges and recommendations. © 2019, Springer-Verlag London Ltd., part of Springer Nature.
- Authors: Razzak, Muhammad , Imran, Muhammad , Xu, Guandong
- Date: 2020
- Type: Text , Journal article
- Relation: Neural Computing and Applications Vol. 32, no. 9 (2020), p. 4417-4451
- Full Text:
- Reviewed:
- Description: Medical data is one of the most rewarding and yet most complicated data to analyze. How can healthcare providers use modern data analytics tools and technologies to analyze and create value from complex data? Data analytics, with its promise to efficiently discover valuable pattern by analyzing large amount of unstructured, heterogeneous, non-standard and incomplete healthcare data. It does not only forecast but also helps in decision making and is increasingly noticed as breakthrough in ongoing advancement with the goal is to improve the quality of patient care and reduces the healthcare cost. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of data analytics methods for disease prevention. This review first introduces disease prevention and its challenges followed by traditional prevention methodologies. We summarize state-of-the-art data analytics algorithms used for classification of disease, clustering (unusually high incidence of a particular disease), anomalies detection (detection of disease) and association as well as their respective advantages, drawbacks and guidelines for selection of specific model followed by discussion on recent development and successful application of disease prevention methods. The article concludes with open research challenges and recommendations. © 2019, Springer-Verlag London Ltd., part of Springer Nature.
Refining Parkinson’s neurological disorder identification through deep transfer learning
- Naseer, Amina, Rani, Monai, Naz, Saeeda, Razzak, Muhammad, Imran, Muhammad, Xu, Guandong
- Authors: Naseer, Amina , Rani, Monai , Naz, Saeeda , Razzak, Muhammad , Imran, Muhammad , Xu, Guandong
- Date: 2020
- Type: Text , Journal article
- Relation: Neural Computing and Applications Vol. 32, no. 3 (2020), p. 839-854
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- Reviewed:
- Description: Parkinson’s disease (PD), a multi-system neurodegenerative disorder which affects the brain slowly, is characterized by symptoms such as muscle stiffness, tremor in the limbs and impaired balance, all of which tend to worsen with the passage of time. Available treatments target its symptoms, aiming to improve the quality of life. However, automatic diagnosis at early stages is still a challenging medicine-related task to date, since a patient may have an identical behavior to that of a healthy individual at the very early stage of the disease. Parkinson’s disease detection through handwriting data is a significant classification problem for identification of PD at the infancy stage. In this paper, a PD identification is realized with help of handwriting images that help as one of the earliest indicators for PD. For this purpose, we proposed a deep convolutional neural network classifier with transfer learning and data augmentation techniques to improve the identification. Two approaches like freeze and fine-tuning of transfer learning are investigated using ImageNet and MNIST dataset as source task independently. A trained network achieved 98.28% accuracy using fine-tuning-based approach using ImageNet and PaHaW dataset. Experimental results on benchmark dataset reveal that the proposed approach provides better detection of Parkinson’s disease as compared to state-of-the-art work. © 2019, Springer-Verlag London Ltd., part of Springer Nature.
- Authors: Naseer, Amina , Rani, Monai , Naz, Saeeda , Razzak, Muhammad , Imran, Muhammad , Xu, Guandong
- Date: 2020
- Type: Text , Journal article
- Relation: Neural Computing and Applications Vol. 32, no. 3 (2020), p. 839-854
- Full Text:
- Reviewed:
- Description: Parkinson’s disease (PD), a multi-system neurodegenerative disorder which affects the brain slowly, is characterized by symptoms such as muscle stiffness, tremor in the limbs and impaired balance, all of which tend to worsen with the passage of time. Available treatments target its symptoms, aiming to improve the quality of life. However, automatic diagnosis at early stages is still a challenging medicine-related task to date, since a patient may have an identical behavior to that of a healthy individual at the very early stage of the disease. Parkinson’s disease detection through handwriting data is a significant classification problem for identification of PD at the infancy stage. In this paper, a PD identification is realized with help of handwriting images that help as one of the earliest indicators for PD. For this purpose, we proposed a deep convolutional neural network classifier with transfer learning and data augmentation techniques to improve the identification. Two approaches like freeze and fine-tuning of transfer learning are investigated using ImageNet and MNIST dataset as source task independently. A trained network achieved 98.28% accuracy using fine-tuning-based approach using ImageNet and PaHaW dataset. Experimental results on benchmark dataset reveal that the proposed approach provides better detection of Parkinson’s disease as compared to state-of-the-art work. © 2019, Springer-Verlag London Ltd., part of Springer Nature.
Efficient brain tumor segmentation with multiscale two-pathway-group conventional neural networks
- Razzak, Muhammad, Imran, Muhammad, Xu, Guandong
- Authors: Razzak, Muhammad , Imran, Muhammad , Xu, Guandong
- Date: 2019
- Type: Text , Journal article
- Relation: IEEE Journal of Biomedical and Health Informatics Vol. 23, no. 5 (2019), p. 1911-1919
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- Reviewed:
- Description: Manual segmentation of the brain tumors for cancer diagnosis from MRI images is a difficult, tedious, and time-consuming task. The accuracy and the robustness of brain tumor segmentation, therefore, are crucial for the diagnosis, treatment planning, and treatment outcome evaluation. Mostly, the automatic brain tumor segmentation methods use hand designed features. Similarly, traditional methods of deep learning such as convolutional neural networks require a large amount of annotated data to learn from, which is often difficult to obtain in the medical domain. Here, we describe a new model two-pathway-group CNN architecture for brain tumor segmentation, which exploits local features and global contextual features simultaneously. This model enforces equivariance in the two-pathway CNN model to reduce instabilities and overfitting parameter sharing. Finally, we embed the cascade architecture into two-pathway-group CNN in which the output of a basic CNN is treated as an additional source and concatenated at the last layer. Validation of the model on BRATS2013 and BRATS2015 data sets revealed that embedding of a group CNN into a two pathway architecture improved the overall performance over the currently published state-of-the-art while computational complexity remains attractive. © 2013 IEEE.
- Authors: Razzak, Muhammad , Imran, Muhammad , Xu, Guandong
- Date: 2019
- Type: Text , Journal article
- Relation: IEEE Journal of Biomedical and Health Informatics Vol. 23, no. 5 (2019), p. 1911-1919
- Full Text:
- Reviewed:
- Description: Manual segmentation of the brain tumors for cancer diagnosis from MRI images is a difficult, tedious, and time-consuming task. The accuracy and the robustness of brain tumor segmentation, therefore, are crucial for the diagnosis, treatment planning, and treatment outcome evaluation. Mostly, the automatic brain tumor segmentation methods use hand designed features. Similarly, traditional methods of deep learning such as convolutional neural networks require a large amount of annotated data to learn from, which is often difficult to obtain in the medical domain. Here, we describe a new model two-pathway-group CNN architecture for brain tumor segmentation, which exploits local features and global contextual features simultaneously. This model enforces equivariance in the two-pathway CNN model to reduce instabilities and overfitting parameter sharing. Finally, we embed the cascade architecture into two-pathway-group CNN in which the output of a basic CNN is treated as an additional source and concatenated at the last layer. Validation of the model on BRATS2013 and BRATS2015 data sets revealed that embedding of a group CNN into a two pathway architecture improved the overall performance over the currently published state-of-the-art while computational complexity remains attractive. © 2013 IEEE.
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