11. Dense CNN approach for medical diagnosis
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Purva Ekatpure
Abstract
Healthcare is one of the domains which has truly advanced to use the latest technologies like machine learning to help in diagnosis. Owing to its complexity of medical images, extracting features correctly makes the problem even tougher. The earlier image processing algorithms using descriptors were unable to detect the disease accurately, and also, using the correct form of descriptors based on the dataset was even a bigger challenge which further reduced the accuracy of the machine learning algorithms trained over the dataset. However the recent advancements in the field of deep learning are able to give better results for these classifications. Convolutional neural networks (CNNs) have proved to be a great algorithm choice in case of extracting spatial features, making it suitable for the medical diagnosis. However, as the number of layers increase in CNNs, the complexity of the network increases and the information passed from one later to another eventually decreases, thus causing information loss. In order to overcome this dense CNN can be considered. We have shown couple of case studies which were performed. One using the local binary patterns and other using dense CNN on two different types of medical images. The dense CNN work was also recognized by the IEEE Computer Society.
Abstract
Healthcare is one of the domains which has truly advanced to use the latest technologies like machine learning to help in diagnosis. Owing to its complexity of medical images, extracting features correctly makes the problem even tougher. The earlier image processing algorithms using descriptors were unable to detect the disease accurately, and also, using the correct form of descriptors based on the dataset was even a bigger challenge which further reduced the accuracy of the machine learning algorithms trained over the dataset. However the recent advancements in the field of deep learning are able to give better results for these classifications. Convolutional neural networks (CNNs) have proved to be a great algorithm choice in case of extracting spatial features, making it suitable for the medical diagnosis. However, as the number of layers increase in CNNs, the complexity of the network increases and the information passed from one later to another eventually decreases, thus causing information loss. In order to overcome this dense CNN can be considered. We have shown couple of case studies which were performed. One using the local binary patterns and other using dense CNN on two different types of medical images. The dense CNN work was also recognized by the IEEE Computer Society.
Kapitel in diesem Buch
- Frontmatter I
- Preface VII
- Contents XI
- List of contributors XIII
- 1. A review of bone tissue engineering for the application of artificial intelligence in cellular adhesion prediction 1
- 2. Implementation and classification of machine learning algorithms in healthcare informatics: approaches, challenges, and future scope 21
- 3. Cardiac arrhythmia recognition using Stockwell transform and ABC-optimized twin SVM 35
- 4. Computational intelligence approach to address the language barrier in healthcare 53
- 5. Recent advancement of machine learning and deep learning in the field of healthcare system 77
- 6. Predicting psychological disorders using machine learning 99
- 7. Automatic analysis of cardiovascular diseases using EMD and support vector machines 131
- 8. Machine learning approach for exploring computational intelligence 153
- 9. Classification of various image fusion algorithms and their performance evaluation metrics 179
- 10. Recommender system in healthcare: an overview 199
- 11. Dense CNN approach for medical diagnosis 217
- 12. Impact of sentiment analysis tools to improve patients’ life in critical diseases 239
- 13. A fuzzy entropy-based multilevel image thresholding using neural network optimization algorithm 253
- 14. Machine learning in healthcare 277
- 15. Computational health informatics using evolutionary-based feature selection 309
- Index 329
Kapitel in diesem Buch
- Frontmatter I
- Preface VII
- Contents XI
- List of contributors XIII
- 1. A review of bone tissue engineering for the application of artificial intelligence in cellular adhesion prediction 1
- 2. Implementation and classification of machine learning algorithms in healthcare informatics: approaches, challenges, and future scope 21
- 3. Cardiac arrhythmia recognition using Stockwell transform and ABC-optimized twin SVM 35
- 4. Computational intelligence approach to address the language barrier in healthcare 53
- 5. Recent advancement of machine learning and deep learning in the field of healthcare system 77
- 6. Predicting psychological disorders using machine learning 99
- 7. Automatic analysis of cardiovascular diseases using EMD and support vector machines 131
- 8. Machine learning approach for exploring computational intelligence 153
- 9. Classification of various image fusion algorithms and their performance evaluation metrics 179
- 10. Recommender system in healthcare: an overview 199
- 11. Dense CNN approach for medical diagnosis 217
- 12. Impact of sentiment analysis tools to improve patients’ life in critical diseases 239
- 13. A fuzzy entropy-based multilevel image thresholding using neural network optimization algorithm 253
- 14. Machine learning in healthcare 277
- 15. Computational health informatics using evolutionary-based feature selection 309
- Index 329
