237 research outputs found
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
Blended Multi-Modal Deep ConvNet Features for Diabetic Retinopathy Severity Prediction
Diabetic Retinopathy (DR) is one of the major causes of visual impairment and
blindness across the world. It is usually found in patients who suffer from
diabetes for a long period. The major focus of this work is to derive optimal
representation of retinal images that further helps to improve the performance
of DR recognition models. To extract optimal representation, features extracted
from multiple pre-trained ConvNet models are blended using proposed multi-modal
fusion module. These final representations are used to train a Deep Neural
Network (DNN) used for DR identification and severity level prediction. As each
ConvNet extracts different features, fusing them using 1D pooling and cross
pooling leads to better representation than using features extracted from a
single ConvNet. Experimental studies on benchmark Kaggle APTOS 2019 contest
dataset reveals that the model trained on proposed blended feature
representations is superior to the existing methods. In addition, we notice
that cross average pooling based fusion of features from Xception and VGG16 is
the most appropriate for DR recognition. With the proposed model, we achieve an
accuracy of 97.41%, and a kappa statistic of 94.82 for DR identification and an
accuracy of 81.7% and a kappa statistic of 71.1% for severity level prediction.
Another interesting observation is that DNN with dropout at input layer
converges more quickly when trained using blended features, compared to the
same model trained using uni-modal deep features.Comment: 18 pages, 8 figures, published in Electronics MDPI journa
Enhanced Ai-Based Machine Learning Model for an Accurate Segmentation and Classification Methods
Phone Laser Scanner becomes the versatile sensor module that is premised on Lamp Identification and Spanning methodology and is used in a spectrum of uses. There are several prior editorials in the literary works that concentrate on the implementations or attributes of these processes; even so, evaluations of all those inventive computational techniques reported in the literature have not even been performed in the required thickness. At ToAT that finish, we examine and summarize the latest advances in Artificial Intelligence based machine learning data processing approaches such as extracting features, fragmentation, machine vision, and categorization. In this survey, we have reviewed total 48 papers based on an enhanced AI based machine learning model for accurate classification and segmentation methods. Here, we have reviewed the sections on segmentation and classification of images based on machine learning models
Robust Retinal Vessel Segmentation using ELM and SVM Classifier
The diagnosis of retinal blood vessels is of much clinical importance, as they are generally examined to evaluate and monitor both the ophthalmological diseases and the non-retinal diseases. The vascular nature of retinal is very complex and the manual segmentation process is tedious. It requires more time and skill. In this paper, a novel supervised approach using Extreme Learning Machine (ELM) classifier and Support Vector Machine (SVM) classifier is proposed to segment the retinal blood vessel. This approach calculates 7-D feature vector comprises of green channel intensity, Median-Local Binary Pattern (M-LBP), Stroke Width Transform (SWT) response, Weber�s Local Descriptor (WLD) measure, Frangi�s vesselness measure, Laplacian Of Gaussian (LOG) filter response and morphological bottom-hat transform. This 7-D vector is given as input to the ELM classifier to classify each pixel as vessel or non-vessel. The primary vessel map from the ELM classifier is combined with the ridges detected from the enhanced bottom-hat transformed image. Then the high-level features computed from the combined image are used for final classification using SVM. The performance of this technique was evaluated on the publically available databases like DRIVE, STARE and CHASE-DB1. The result demonstrates that the proposed approach is very fast and achieves high accuracy about 96.1% , 94.4% and 94.5% for DRIVE, STARE and CHASE-DB1 respectively
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