K-Nearest Neighbour Classification and Feature Extraction GLCM for Identification of Terry's Nail

Safira, Laura; Irawan, Budhi; Setianingsih, Casi

doi:10.1109/iciaict.2019.8784856

Cited by 13 publications

(11 citation statements)

References 4 publications

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“…Terry's nails occur in the vast majority of patients diagnosed with cirrhosis of the liver. It is stated that the nail layer on Terry's nail is whitish with a tight pink line (Safira et al, 2019). Onycholysis disease occurs when the nail layer is separated from the nail bed, and this is generally originated from a trauma.…”

Section: Introductionmentioning

confidence: 99%

Detection of Nail Diseases Using Ensemble Model Based on Majority Voting

Ali

Kuyucuoglu

KÖSEOĞLU

et al. 2023

Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi

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Nail diseases are disorders that can have serious effects on human quality of life. With the developing computational methods and technology, anomalies on the nail may be detected quickly and in a non-invasive way. This study proposes a model that provides better performance by combining the results of different deep learning networks with the ensemble learning method. The performance of 7 different deep learning architectures was examined using a database containing 17 disease classes. The proposed method achieved 75 % accuracy, resulting in significant increases in precision and recall metrics compared to individual deep-learning architectures. Thanks to a mobile application that will be developed, the proposed model for large-scale screening may be used as an assistive decision support system for medical professionals. When the results are observed, we predict that early detection of nail diseases (in a remote way) on the hand, which is one of our most used limbs, can reduce hospital visits and costs. In addition, the proposed method can be integrated into dermatoscopy devices used for skin diseases and mole analysis.

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Section: Introductionmentioning

confidence: 99%

Detection of Nail Diseases Using Ensemble Model Based on Majority Voting

Ali

Kuyucuoglu

KÖSEOĞLU

et al. 2023

Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi

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“…In the field of image classification, L. Safira [1] studied nail images. They used a grey level co-occurrence matrix (GLCM) [2][3][4] to extract the texture features of the image and then used k-nearest neighbours (KNN) for classification, finally completing the detection of Terry's nails.…”

Section: Introductionmentioning

confidence: 99%

“…A 50 � 50 pixel block in the effective region of nine categories of template images is extracted, as shown in Figure 2. For each type of picture, an RGB three-channel average is extracted to form three 3 � 3 colour matrices, as shown in Equation (1).…”

Section: Introductionmentioning

confidence: 99%

Classification and detection using hidden Markov model‐support vector machine algorithm based on optimal colour space selection for blood images

Wang

Song

Sun

2022

Cognitive Comp and Systems

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Patients with cerebral haemorrhages need to drain haematomas. Fresh blood may appear during the haematoma drainage process, so this needs to be observed and detected in real time. To solve this problem, this paper studies images produced during the haematoma drainage process. A blood image feature selection recognition and classification framework is designed. First, aiming at the characteristics of the small colour differences in blood images, the general RGB colour space feature is not obvious. This study proposes an optimal colour channel selection method. By extracting the colour information from the images, it is recombined into a 3 × 3 matrix. The normalised 4‐neighbourhood contrast and variance are calculated for quantitative comparison. The optimised colour channel is selected to overcome the problem of weak features caused by a single colour space. After that, the effective region in the image is intercepted, and the best colour channel of the image in the region is transformed. The first, second and third moments of the three best colour channels are extracted to form a nine‐dimensional eigenvector. K‐means clustering is used to obtain the image eigenvector, outliers are removed, and the results are then transferred to the hidden Markov model (HMM) and support vector machine (SVM) for classification. After selecting the best color channel, the classification accuracy of HMM‐SVM is greatly improved. Compared with other classification algorithms, the proposed method offers great advantages. Experiments show that the recognition accuracy of this method reaches 98.9%.

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“…Haralick et al report that the GLCM is a method to quantify the spatial relationship between adjacent pixels in an image [23]. GLCM is widely used in disease detection [24]- [26], skin texture analysis [27], defect detection [28], fabric classification [29], egg fertility identification [30], etc. Safira et.al report a research to detect abnormalities of nails.…”

mentioning

confidence: 99%

“…Safira et.al report a research to detect abnormalities of nails. They extract textural characteristics processed with GLCM of the image data and classifying method KNN is chosen to classify the abnormal nails [26]. Gustian et.al classify troso fabrics using a combination of two methods, Support Vector Machine (SVM) with Ones Against All and SVM with Ones Against One, based on GLCM and Principle Component Analysis (PCA).…”

mentioning

confidence: 99%

Face Mask Assistant: Detection of Face Mask Service Stage Based on Mobile Phone

Chen

Hua

et al. 2021

IEEE Sensors J.

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Coronavirus Disease 2019 (COVID-19) has spread all over the world since it broke out massively in December 2019, which has caused a large loss to the whole world. Both the confirmed cases and death cases have reached a relatively frightening number. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of COVID-19, can be transmitted by small respiratory droplets. To curb its spread at the source, wearing masks is a convenient and effective measure. In most cases, people use face masks in a high-frequent but short-time way. Aimed at solving the problem that we do not know which service stage of the mask belongs to, we propose a detection system based on the mobile phone. We first extract four features from the gray level co-occurrence matrixes (GLCMs) of the face mask's micro-photos. Next, a three-result detection system is accomplished by using K Nearest Neighbor (KNN) algorithm. The results of validation experiments show that our system can reach an accuracy of 82.87% (measured by macro-measures) on the testing dataset. The precision of Type I 'normal use' and the recall of type III 'not recommended' reach 92.00% and 92.59%. In future work, we plan to expand the detection objects to more mask types. This work demonstrates that the proposed mobile microscope system can be used as an assistant for face mask being used, which may play a positive role in fighting against COVID-19. Index Terms-COVID-19 pandemic, use time of face mask, textural feature, SARS-CoV-2, machine learning, image processing. I. INTRODUCTIONC OVID-19 has wreaked havoc around the world and the spread of COVID-19 has not been curbed totally by June, 2020. According to the situation report of the World Health Manuscript

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K-Nearest Neighbour Classification and Feature Extraction GLCM for Identification of Terry's Nail

Cited by 13 publications

References 4 publications

Detection of Nail Diseases Using Ensemble Model Based on Majority Voting

Detection of Nail Diseases Using Ensemble Model Based on Majority Voting

Classification and detection using hidden Markov model‐support vector machine algorithm based on optimal colour space selection for blood images

Face Mask Assistant: Detection of Face Mask Service Stage Based on Mobile Phone

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