False-positive reduction in computer-aided mass detection using mammographic texture analysis and classification

Barhoumi, Walid; Kurek, Jarosław; Świderski, Bartosz; Kruk, Michał; Zagrouba, Ezzeddine

doi:10.1016/j.cmpb.2018.03.026

Cited by 25 publications

(17 citation statements)

References 23 publications

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“…The use of fractal curves, and in particular the Hilbert curve, for designing alternative image and signal representations is long known, but never studied for sEMG signals. In [30] and [31], the properties of the Hilbert curve have been exploited to convert mammographic images to 1D vectors. In combination with a set of appropriate features, this helped in detecting breast can-cer.…”

Section: Related Workmentioning

confidence: 99%

The Effect of Space-filling Curves on the Efficiency of Hand Gesture Recognition Based on sEMG Signals

Tsinganos

Cornelis

et al. 2021

Int. j. electr. comput. eng. syst. (Online)

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Over the past few years, Deep learning (DL) has revolutionized the field of data analysis. Not only are the algorithmic paradigms changed, but also the performance in various classification and prediction tasks has been significantly improved with respect to the state-of-the-art, especially in the area of computer vision. The progress made in computer vision has produced a spillover in many other domains, such as biomedical engineering. Some recent works are directed towards surface electromyography (sEMG) based hand gesture recognition, often addressed as an image classification problem and solved using tools such as Convolutional Neural Networks (CNN). This paper extends our previous work on the application of the Hilbert space-filling curve for the generation of image representations from multi-electrode sEMG signals, by investigating how the Hilbert curve compares to the Peano- and Z-order space-filling curves. The proposed space-filling mapping methods are evaluated on a variety of network architectures and in some cases yield a classification improvement of at least 3%, when used to structure the inputs before feeding them into the original network architectures.

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Section: Related Workmentioning

confidence: 99%

The Effect of Space-filling Curves on the Efficiency of Hand Gesture Recognition Based on sEMG Signals

Tsinganos

Cornelis

et al. 2021

Int. j. electr. comput. eng. syst. (Online)

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“…The classification of data using ensemble classifiers in this study reveals the characterization accuracy of about 81% for Fibro-Fatty Tissue (FFT) and 75% for Necrotic Core (NC) images. In some cases, GLCM is often combined with other texture extracting methods to obtain higher accuracy (Dhahbi et al, 2018). It is possible to combine GLCM with other handcrafted feature extraction not only texture-based but also shape-based or color-based.…”

Section: Handcrafted Feature Extractionmentioning

confidence: 99%

Multipatch-GLCM for Texture Feature Extraction on Classification of the Colon Histopathology Images using Deep Neural Network with GPU Acceleration

Haryanto¹,

Pratama²,

Suhartanto³

et al. 2020

Journal of Computer Science

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Cancer is one of the leading causes of death in the world. It is the main reason why research in this field becomes challenging. Not only for the pathologist but also from the view of a computer scientist. Hematoxylin and Eosin (H&E) images are the most common modalities used by the pathologist for cancer detection. The status of cancer with histopathology images can be classified based on the shape, morphology, intensity, and texture of the image. The use of full high-resolution histopathology images will take a longer time for the extraction of all information due to the huge amount of data. This study proposed advance texture extraction by multi-patch images pixel method with sliding windows that minimize loss of information in each pixel patch. We use texture feature Gray Level Co-Occurrence Matrix (GLCM) with a meanshift filter as the data pre-processing of the images. The mean-shift filter is a low-pass filter technique that considers the surrounding pixels of the images. The proposed GLCM method is then trained using Deep Neural Networks (DNN) and compared to other classification techniques for benchmarking. For training, we use two hardware: NVIDIA GPU GTX-980 and TESLA K40c. According to the study, Deep Neural Network outperforms other classifiers with the highest accuracy and deviation standard 96.72±0.48 for four cross-validations. The additional information is that training using Theano framework is faster than Tensorflow for both in GTX-980 and Tesla K40c.

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“…The properties of the Hilbert curve are well known and have been exploited in the past for diverse applications. The authors of [13,29] employ the Hilbert curve to represent mammographic images as 1D vectors from which a combination of features is extracted in order to detect breast cancer. Similarly, the work of [11] transforms volumetric data into 2D and 1D representations, which are then processed efficiently by typical CNNs.…”

Section: Related Workmentioning

confidence: 99%

Hilbert sEMG data scanning for hand gesture recognition based on deep learning

Tsinganos

Cornelis

et al. 2020

Neural Comput & Applic

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Deep learning has transformed the field of data analysis by dramatically improving the state of the art in various classification and prediction tasks, especially in the area of computer vision. In biomedical engineering, a lot of new work is directed toward surface electromyography (sEMG)-based gesture recognition, often addressed as an image classification problem using convolutional neural networks (CNNs). In this paper, we utilize the Hilbert space-filling curve for the generation of image representations of sEMG signals, which allows the application of typical image processing pipelines such as CNNs on sequence data. The proposed method is evaluated on different state-of-the-art network architectures and yields a significant classification improvement over the approach without the Hilbert curve. Additionally, we develop a new network architecture (MSHilbNet) that takes advantage of multiple scales of an initial Hilbert curve representation and achieves equal performance with fewer convolutional layers.

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False-positive reduction in computer-aided mass detection using mammographic texture analysis and classification

Cited by 25 publications

References 23 publications

The Effect of Space-filling Curves on the Efficiency of Hand Gesture Recognition Based on sEMG Signals

The Effect of Space-filling Curves on the Efficiency of Hand Gesture Recognition Based on sEMG Signals

Multipatch-GLCM for Texture Feature Extraction on Classification of the Colon Histopathology Images using Deep Neural Network with GPU Acceleration

Hilbert sEMG data scanning for hand gesture recognition based on deep learning

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