Multilevel Feature Extraction and X-ray Image Classification

Mueen, Ahmed; Baba, Mohd Sapiyan; Zainuddin, Roziati

doi:10.3923/jas.2007.1224.1229

Cited by 45 publications

(11 citation statements)

References 11 publications

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“…For example, the scale-invariant feature transform (SIFT) was used to extract image features invariant to changes in scaling orientation and illumination and coupled with the bag-of-visual words (BoVW) model to form a histogram representation of the image [26,27]. Other common approaches used a combination of multi-visual features including local binary patterns, texture and shape [28]. Avni et al [26] presented densely sampled normalised features coupled with spatial information for X-ray categorisation and retrieval.…”

Section: A X-ray Image Retrievalmentioning

confidence: 99%

Convolutional sparse kernel network for unsupervised medical image analysis

Ahn

Kumar

Fulham

et al. 2019

Medical Image Analysis

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Section: A X-ray Image Retrievalmentioning

confidence: 99%

Convolutional sparse kernel network for unsupervised medical image analysis

Ahn

Kumar

Fulham

et al. 2019

Medical Image Analysis

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“…A set of features plays a significant role in image classification. The following section discusses a collection of various features that other researchers have used to classify medical images (A Mueen et al 2007 ).…”

Section: Introductionmentioning

confidence: 99%

Classification of spinal curvature types using radiography images: deep learning versus classical methods

et al. 2023

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Scoliosis is a spinal abnormality that has two types of curves (C-shaped or S-shaped). The vertebrae of the spine reach an equilibrium at different times, which makes it challenging to detect the type of curves. In addition, it may be challenging to detect curvatures due to observer bias and image quality. This paper aims to evaluate spinal deformity by automatically classifying the type of spine curvature. Automatic spinal curvature classification is performed using SVM and KNN algorithms, and pre-trained Xception and MobileNetV2 networks with SVM as the final activation function to avoid vanishing gradient. Different feature extraction methods should be used to investigate the SVM and KNN machine learning methods in detecting the curvature type. Features are extracted through the representation of radiographic images. These representations are of two groups: (i) Low-level image representation techniques such as texture features and (ii) local patch-based representations such as Bag of Words (BoW). Such features are utilized by various algorithms for classification by SVM and KNN. The feature extraction process is automated in pre-trained deep networks. In this study, 1000 anterior–posterior (AP) radiographic images of the spine were collected as a private dataset from Shafa Hospital, Tehran, Iran. The transfer learning was used due to the relatively small private dataset of anterior–posterior radiology images of the spine. Based on the results of these experiments, pre-trained deep networks were found to be approximately 10% more accurate than classical methods in classifying whether the spinal curvature is C-shaped or S-shaped. As a result of automatic feature extraction, it has been found that the pre-trained Xception and mobilenetV2 networks with SVM as the final activation function for controlling the vanishing gradient perform better than the classical machine learning methods of classification of spinal curvature types.

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“…Experimental results proved the superiority of ASFDA among some state-of-the-art methods [ 20 ]. Mueen et al extracted three levels of features global, local, and pixel and combined them together in one big feature vector that achieved a recognition rate of 89% [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction and Classification on Esophageal X-Ray Images of Xinjiang Kazak Nationality

Yang

Hamit

Yan

et al. 2017

Journal of Healthcare Engineering

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Esophageal cancer is one of the fastest rising types of cancers in China. The Kazak nationality is the highest-risk group in Xinjiang. In this work, an effective computer-aided diagnostic system is developed to assist physicians in interpreting digital X-ray image features and improving the quality of diagnosis. The modules of the proposed system include image preprocessing, feature extraction, feature selection, image classification, and performance evaluation. 300 original esophageal X-ray images were resized to a region of interest and then enhanced by the median filter and histogram equalization method. 37 features from textural, frequency, and complexity domains were extracted. Both sequential forward selection and principal component analysis methods were employed to select the discriminative features for classification. Then, support vector machine and K-nearest neighbors were applied to classify the esophageal cancer images with respect to their specific types. The classification performance was evaluated in terms of the area under the receiver operating characteristic curve, accuracy, precision, and recall, respectively. Experimental results show that the classification performance of the proposed system outperforms the conventional visual inspection approaches in terms of diagnostic quality and processing time. Therefore, the proposed computer-aided diagnostic system is promising for the diagnostics of esophageal cancer.

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Multilevel Feature Extraction and X-ray Image Classification

Cited by 45 publications

References 11 publications

Convolutional sparse kernel network for unsupervised medical image analysis

Convolutional sparse kernel network for unsupervised medical image analysis

Classification of spinal curvature types using radiography images: deep learning versus classical methods

Feature Extraction and Classification on Esophageal X-Ray Images of Xinjiang Kazak Nationality

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