Prototype system for feature extraction, classification and study of medical images

Oliva, Jefferson Tales; Lee, Huei; Spolaôr, Newton; Coy, Cláudio Saddy Rodrigues; Wu, Feng

doi:10.1016/j.eswa.2016.07.008

Cited by 22 publications

(7 citation statements)

References 29 publications

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“…The region of interest is homogeneous when the matrix is wide and flat, and it is heterogeneous when the matrix is narrow. The NGTDM [6,27] quantifies the difference between a gray value and the average gray value of its neighbors within a certain distance. The GLDM [28] quantifies gray level dependencies in an image, which are defined as the number of connected voxels within a certain distance that are dependent on the center voxel.…”

Section: Pre-processing Technique Effect On Imagementioning

confidence: 99%

A deep look into radiomics

et al. 2021

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Radiomics is a process that allows the extraction and analysis of quantitative data from medical images. It is an evolving field of research with many potential applications in medical imaging. The purpose of this review is to offer a deep look into radiomics, from the basis, deeply discussed from a technical point of view, through the main applications, to the challenges that have to be addressed to translate this process in clinical practice. A detailed description of the main techniques used in the various steps of radiomics workflow, which includes image acquisition, reconstruction, pre-processing, segmentation, features extraction and analysis, is here proposed, as well as an overview of the main promising results achieved in various applications, focusing on the limitations and possible solutions for clinical implementation. Only an in-depth and comprehensive description of current methods and applications can suggest the potential power of radiomics in fostering precision medicine and thus the care of patients, especially in cancer detection, diagnosis, prognosis and treatment evaluation.

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Section: Pre-processing Technique Effect On Imagementioning

confidence: 99%

A deep look into radiomics

et al. 2021

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“…Three different descriptors with three spatial resolutions are used to evaluate the proposed approach in texture images. In [18], a J48 classifier is built to evaluate colonoscopy exam images using texture descriptors. As shown in the results, the proposed classifier has achieved a relative high sensitivity.…”

Section: Related Workmentioning

confidence: 99%

Image Classification With Convolutional Neural Networks In MapReduce

Chen

2021

Preprint

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Deep learning (DL) techniques, more specifically Convolutional Neural Networks (CNNs), have become increasingly popular in advancing the field of data science and have had great successes in a wide array of applications including computer vision, speech, natural language processing and etc. However, the training process of CNNs is computationally intensive and high computational cost, especially when the dataset is huge. To overcome these obstacles, this paper takes advantage of distributed frameworks and cloud computing to develop a parallel CNN algorithm. MapReduce is a scalable and fault-tolerant data processing tool that was developed to provide significant improvements in large-scale data-intensive applications in clusters. A MapReduce-based CNN (MCNN) is developed in this work to tackle the task of image classification. In addition, the proposed MCNN adopted the idea of adding dropout layers in the networks to tackle the overfitting problem. Close examination of the implementation of MCNN as well as how the proposed algorithm accelerates learning are discussed and demonstrated through experiments. Results reveal high classification accuracy and significant improvements in speedup, scaleup and sizeup compared to the standard algorithms.

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“…For image classification through texture descriptors, the literature presents the improvement of local binary patterns in texture analysis ( Kwak, Xu, & Wood, 2015 ). In are evaluated feature from images using descriptors ( Oliva, Lee, Spolaôr, Coy, & Wu, 2016 ), proposes a method based on the use of scale invariant features transform ( Abdolshah, Teimouri, & Rahmani, 2017 ) for classifications containers x-ray images. In the table is presented the main contributions in this area.…”

Section: Related Workmentioning

confidence: 99%

Deep learning for biological image classification

Affonso

Rossi

Vieira

et al. 2017

Expert Systems with Applications

207

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A number of industries use human inspection to visually classify the quality of their products and the raw materials used in the production process, this process could be done automatically through digital image processing. The industries are not always interested in the most accurate technique for a given problem, but most appropriate for the expected results, there must be a balance between accuracy and computational cost. This paper investigates the classification of the quality of wood boards based on their images. For such, it compares the use of deep learning, particularly Convolutional Neural Networks, with the combination of texture-based feature extraction techniques and traditional techniques: Decision tree induction algorithms, Neural Networks, Nearest neighbors and Support vector machines. Reported studies show that Deep Learning techniques applied to image processing tasks have achieved predictive performance superior to traditional classification techniques, mainly in high complex scenarios. One of the reasons pointed out is their embedded feature extraction mechanism. Deep Learning techniques directly identify and extract features, considered by them to be relevant, in a given image dataset. However, empirical results for the image data set have shown that the texture descriptor method proposed, regardless of the strategy employed is very competitive when compared with Convolutional Neural Network for all the performed experiments. The best performance of the texture descriptor method could be caused by the nature of the image dataset. Finally are pointed out some perspectives of futures developments with the application of Active learning and Semi supervised methods.

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Prototype system for feature extraction, classification and study of medical images

Cited by 22 publications

References 29 publications

A deep look into radiomics

A deep look into radiomics

Image Classification With Convolutional Neural Networks In MapReduce

Deep learning for biological image classification

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