A Novel Computer-Aided Diagnosis Framework Using Deep Learning for Classification of Fatty Liver Disease in Ultrasound Imaging

2018 ◽  
Author(s):  
D Santhosh Reddy ◽  
R Bharath ◽  
P Rajalakshmi
Keyword(s):  
Deep Learning ◽  
Liver Disease ◽  
Fatty Liver ◽  
Ultrasound Imaging ◽  
Fatty Liver Disease ◽  
Computer Aided Diagnosis ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals Computer-Aided Diagnosis Approaches to Fatty Liver Disease According to Sonographic Images Based on Wavelet Transform: A Review Study

2018 ◽  
Vol 3 (4) ◽  
Keyword(s):  
Image Processing ◽  
Wavelet Transform ◽  
Liver Disease ◽  
Fatty Liver ◽  
Fatty Liver Disease ◽  
Wavelet Transformation ◽  
Statistical Features ◽  
Computer Aided ◽  
Review Study ◽  
Aided Diagnosis

Introduction: Fatty liver is usually diagnosed by ultrasound, but this diagnosis can be difficult because the disease does not always lead to abnormal conditions on gray levels that can be detected by the eye. However, ultrasound is still the first choice to detect fatty liver due to its low cost and availability, and the lack of side effects. The study reviewed Computer-Aided Diagnosis approaches to fatty liver disease, based on wavelet transform sonographic image processing. Methods: In this review study, a search was conducted based on related keywords and articles that had been published in English over the last 12 years. The findings were extracted based on the aim of study. Findings: Nowadays wavelet transformation has been widely used in the field of medical image processing because of its adaptability to the characteristics of the human eye system. The well-known wavelets used to liver diseases detection include Haar, Symlet, Daubechies and Gabor. Extracting the proper properties of images plays an important role in detecting diseases. Important statistical features of image textures are: statistical descriptors based on the intensity histogram and the GLCM matrix (Gray level Co-occurrence Matrix). The popular algorithms used for liver disease include neural network, Support Vector Machine (SVM), Bayesian, decision tree, K-Nearest Neighbor (KNN), and regression. Conclusion: The sensitivity, specificity and accuracy of the extracted statistical features of the output components of wavelet transform are generally better than those obtained from the original image itself. Gabor’s wavelet transformation often has a higher efficiency than the Daubechies and Symlet wavelet transforms because the two transforms only break up the halfband of low frequencies and lose some of the intermediate frequency regions, while Gabor retains all of the frequency regions This precision also mainly depends on the type of features selected and the type of classification. Statistical features based on intensity histograms do not provide relative information about the spatial of pixels relative to each other. To enter this spatial information of pixels in a texture analysis, it is recommended to use GLCM matrix in gray images. The type of classifier used can significantly impact on the precision of the final diagnosis.

Breast Cancer Detection and Classification using Traditional Computer Vision Techniques: A Comprehensive Review

2020 ◽  
Vol 16 ◽  
Author(s):  
Saliha Zahoor ◽  
Ikram Ullah Lali ◽  
Muhammad Attique Khan ◽  
Kashif Javed ◽  
Waqar Mehmood
Keyword(s):  
Breast Cancer ◽  
Computer Aided Diagnosis ◽  
Diagnose Breast Cancer ◽  
Initial Stage ◽  
Computer Aided ◽  
Diagnosis And Classification ◽  
And Performance ◽  
Tools And Techniques ◽  
Aided Diagnosis

: Breast Cancer is a common dangerous disease for women. In the world, many women died due to Breast cancer. However, in the initial stage, the diagnosis of breast cancer can save women's life. To diagnose cancer in the breast tissues there are several techniques and methods. The image processing, machine learning and deep learning methods and techniques are presented in this paper to diagnose the breast cancer. This work will be helpful to adopt better choices and reliable methods to diagnose breast cancer in an initial stage to survive the women's life. To detect the breast masses, microcalcifications, malignant cells the different techniques are used in the Computer-Aided Diagnosis (CAD) systems phases like preprocessing, segmentation, feature extraction, and classification. We have been reported a detailed analysis of different techniques or methods with their usage and performance measurement. From the reported results, it is concluded that for the survival of women’s life it is essential to improve the methods or techniques to diagnose breast cancer at an initial stage by improving the results of the Computer-Aided Diagnosis systems. Furthermore, segmentation and classification phases are challenging for researchers for the diagnosis of breast cancer accurately. Therefore, more advanced tools and techniques are still essential for the accurate diagnosis and classification of breast cancer.

scholarly journals Quantifying the uncertainty of deep learning-based computer-aided diagnosis for patient safety

2019 ◽  
Vol 5 (1) ◽  
pp. 223-226
Author(s):  
Max-Heinrich Laves ◽  
Sontje Ihler ◽  
Tobias Ortmaier ◽  
Lüder A. Kahrs
Keyword(s):  
Monte Carlo ◽  
Patient Safety ◽  
Deep Learning ◽  
Epistemic Uncertainty ◽  
Computer Aided Diagnosis ◽  
Uncertainty Estimation ◽  
Test Time ◽  
Prediction Uncertainty ◽  
Computer Aided ◽  
Aided Diagnosis

AbstractIn this work, we discuss epistemic uncertainty estimation obtained by Bayesian inference in diagnostic classifiers and show that the prediction uncertainty highly correlates with goodness of prediction. We train the ResNet-18 image classifier on a dataset of 84,484 optical coherence tomography scans showing four different retinal conditions. Dropout is added before every building block of ResNet, creating an approximation to a Bayesian classifier. Monte Carlo sampling is applied with dropout at test time for uncertainty estimation. In Monte Carlo experiments, multiple forward passes are performed to get a distribution of the class labels. The variance and the entropy of the distribution is used as metrics for uncertainty. Our results show strong correlation with ρ = 0.99 between prediction uncertainty and prediction error. Mean uncertainty of incorrectly diagnosed cases was significantly higher than mean uncertainty of correctly diagnosed cases. Modeling of the prediction uncertainty in computer-aided diagnosis with deep learning yields more reliable results and is therefore expected to increase patient safety. This will help to transfer such systems into clinical routine and to increase the acceptance of machine learning in diagnosis from the standpoint of physicians and patients.

scholarly journals The Role and Impact of Deep Learning Methods in Computer-Aided Diagnosis Using Gastrointestinal Endoscopy

Diagnostics ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 694
Author(s):  
Xuejiao Pang ◽  
Zijian Zhao ◽  
Ying Weng
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Clinical Practice ◽  
Gastrointestinal Endoscopy ◽  
Computer Aided Diagnosis ◽  
Learning Methods ◽  
Learning Abilities ◽  
Cad System ◽  
Computer Aided ◽  
Aided Diagnosis

At present, the application of artificial intelligence (AI) based on deep learning in the medical field has become more extensive and suitable for clinical practice compared with traditional machine learning. The application of traditional machine learning approaches to clinical practice is very challenging because medical data are usually uncharacteristic. However, deep learning methods with self-learning abilities can effectively make use of excellent computing abilities to learn intricate and abstract features. Thus, they are promising for the classification and detection of lesions through gastrointestinal endoscopy using a computer-aided diagnosis (CAD) system based on deep learning. This study aimed to address the research development of a CAD system based on deep learning in order to assist doctors in classifying and detecting lesions in the stomach, intestines, and esophagus. It also summarized the limitations of the current methods and finally presented a prospect for future research.

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