Calcification Detection of Coronary Artery Disease in Intravascular Ultrasound Image: Deep Feature Learning Approach

Sofian, Hannah; Than, Joel Chia Ming; Mohammad, Suraya; Noor, Norliza Mohd

doi:10.30880/ijie.2018.10.07.005

Cited by 10 publications

(11 citation statements)

References 22 publications

(23 reference statements)

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“…PPV is the probability that the disease is presence given a positive test result and is defined as TP/TP+FP. Similarly, the NPV is the probability that the disease is absent given a negative test result, and is defined as TN/TN+FN [25].…”

Section: Resultsmentioning

confidence: 99%

Calcification detection for intravascular ultrasound image using direct acyclic graph architecture: pre-trained model for 1-channel image

Sofian

Than

Mohamad

et al. 2021

IJEECS

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Coronary artery calcification is a calcium buildup within the walls of the arteries. It is considered a predominant marker for coronary artery disease. Thus many approaches have been developed for the automatic detection of calcification. The previous calcification detection was on segmentation of other structures as pre-processing steps or using the fact that the calcification often appears as a bright region. In this paper, an automated system proposed using a deep learning approach to detect the calcification absence and calcification presence in coronary artery IVUS image. A useful advantage of deep learning, compared to other methods is, it uses representations and features directly from the raw data, bypassing the need to manually extract features, a common that required in the traditional machine learning framework. The type of deep learning architecture used is 27 layers of convolutional neural networks (CNNs) using Direct Acyclic Graph. The proposed system used 2175 images and achieved an accuracy of 98.16% for Cartesian coordinate images and 99.08% for Polar Reconstructed Coordinate images.

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Section: Resultsmentioning

confidence: 99%

Calcification detection for intravascular ultrasound image using direct acyclic graph architecture: pre-trained model for 1-channel image

Sofian

Than

Mohamad

et al. 2021

IJEECS

Self Cite

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“…The output of the last fully-connected layer consisted of 4096 dimensional features [16]. AlexNet had shown very useful in classification of medical imaging for diseases such as lung diseases [17], heart conditions [18,19] as well as cancer [20].…”

Section: Alexnetmentioning

confidence: 99%

Chest X-Ray Image Classification on Common Thorax Diseases using GLCM and AlexNet Deep Features

Akmal¹,

Than²,

Abdullah³

et al. 2019

IJIE

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Image processing in medical has been progressing far than it ever did when it's one of the main techniques used in the biomedical imaging system and computer aided diagnosis systems. Few of the well-known medical imaging modalities are Magnetic Resonance Imaging (MRI), Computed Tomography (CT) Scan, X-Ray and Ultrasound. The output from these imaging modalities would later be reviewed by expert for an accurate result. Computer aided diagnosis not only save time as it can process thousand images just in a few minutes, it can also be used as tool to aid the expert as a second opinion, which in turn could save more lives. Thorax consist of several organs which include heart, lungs, thymus gland, muscles and other various internal structures such as diaphragm. Thorax disease refer to diseases that affect the chest and the most common symptom is the chest pain. Some of the common thoracic disease are atelectasis, pneumothorax, pneumonia, nodule, mass, infiltration, effusion and cardiomegaly. Figure 1 shows the thorax diseases observed in chest X-ray [1]. Correct diagnosis and disease treatment are important especially during the early stage, which is the most difficult part of the process due to similarity in appearance.

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“…Convolutional neural networks perform a series of convolutions and pooling operations during feature detection and extraction [17]. CNNs generate more discriminative representations compared to traditional methods based on handcrafted features [5], [20].…”

Section: Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Automatic Face and Hijab Segmentation using Convolutional Network

Madkour¹,

Transport²,

Madani³

et al. 2019

IJIE

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Taking pictures and Selfies are now very common and frequent between people. People are also interested in enhancing pictures using different image processing techniques and sharing them on social media. Accurate image segmentation plays an important role in portrait editing, face beautification, human identification, hairstyle identification, airport Surveillance system and many other computer vision problems. One specific functionality of interest is automatic face and veil segmentation as this allows processing each separately. Manual segmentation can be difficult and annoying especially on smartphones small screen. In this paper, the proposed model uses fully convolutional network (FCN) to make semantic segmentation into skin, veil and background. The proposed model achieved an outperforming result on the dataset which consists of 250 images with global accuracy 92% and mean accuracy 92.69.

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Calcification Detection of Coronary Artery Disease in Intravascular Ultrasound Image: Deep Feature Learning Approach

Cited by 10 publications

References 22 publications

Calcification detection for intravascular ultrasound image using direct acyclic graph architecture: pre-trained model for 1-channel image

Calcification detection for intravascular ultrasound image using direct acyclic graph architecture: pre-trained model for 1-channel image

Chest X-Ray Image Classification on Common Thorax Diseases using GLCM and AlexNet Deep Features

Automatic Face and Hijab Segmentation using Convolutional Network

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