Automated Detection of Early Oral Cancer Trends in Habitual Smokers

Dey, Susmita; Tibarewala, D. N.; Maity, Santi P.; Barui, Ananya

doi:10.1016/b978-0-12-813087-2.00005-1

Cited by 3 publications

(4 citation statements)

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“…Texture feature has been one of the most important and most commonly used low level visual features in content-based medical image 19 . Texture descriptors are used to describe the content of medical images 20 and histological images 21 , 22 . Some histological image studies are focused on cell 23 , 24 and in pathologies 25 – 27 .…”

Section: Methodsmentioning

confidence: 99%

Using an ontology of the human cardiovascular system to improve the classification of histological images

Mazo

Alegre

Trujillo

2020

Sci Rep

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The advantages of automatically recognition of fundamental tissues using computer vision techniques are well known, but one of its main limitations is that sometimes it is not possible to classify correctly an image because the visual information is insufficient or the descriptors extracted are not discriminative enough. An Ontology could solve in part this problem, because it gathers and makes possible to use the specific knowledge that allows detecting clear mistakes in the classification, occasionally simply by pointing out impossible configurations in that domain. One of the main contributions of this work is that we used a Histological Ontology to correct, and therefore improve the classification of histological images. First, we described small regions of images, denoted as blocks, using Local Binary Pattern (LBP) based descriptors and we determined which tissue of the cardiovascular system was present using a cascade Support Vector Machine (SVM). Later, we built Resource Description Framework (RDF) triples for the occurrences of each discriminant class. Based on that, we used a Histological Ontology to correct, among others, “not possible” situations, improving in this way the global accuracy in the blocks first and in tissues classification later. For the experimental validation, we used a set of 6000 blocks of pixels, obtaining F-Scores between 0.769 and 0.886. Thus, there is an improvement between 0.003 and when compared to the approach without the histological ontology. The methodology improves the automatic classification of histological images using a histological ontology. Another advantage of our proposal is that using the Ontology, we were capable of recognising the epithelial tissue, previously not detected by any of the computer vision methods used, including a CNN proposal called HistoResNet evaluated in the experiments. Finally, we also have created and made publicly available a dataset consisting of 6000 blocks of labelled histological tissues.

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Section: Methodsmentioning

confidence: 99%

Using an ontology of the human cardiovascular system to improve the classification of histological images

Mazo

Alegre

Trujillo

2020

Sci Rep

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“…Nguyen et al (2018) tested microscopic image as another type of medical image modality that took from 2D-Hela and PAP-smear datasets [38]. Confocal laser endoscopy (CLE) is another medical image modality type that relied on to diagnose and detect brain tumor for its accuracy and effectiveness in carrying out the automatic diagnosis [39]. It is a type of advanced optical fluorescence technology which undergoing application assessments in brain tumor surgery while most of the images distorted and interpreted as non-diagnostic images [40].…”

Section: Medical Image Modalitiesmentioning

confidence: 99%

“…M. hyper-spectral image (HSI) is another new modality use to diagnosis and early detection of oral cancer using CNN before surgery [47]. used it to early detect of oral cancer in habitual smokers [39]. N. Single X-ray projection uses to monitoring and radiotherapy tumortracking to analyze tumor motion [48].…”

Section: Medical Image Modalitiesmentioning

confidence: 99%

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Review Research of Medical Image Analysis Using Deep Learning

Mohammed

Al-Ani

2020

UHD J SCI TECH

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In modern globe, medical image analysis significantly participates in diagnosis process. In general, it involves five processes, such as medical image classification, medical image detection, medical image segmentation, medical image registration, and medical image localization. Medical imaging uses in diagnosis process for most of the human body organs, such as brain tumor, chest, breast, colonoscopy, retinal, and many other cases relate to medical image analysis using various modalities. Multi-modality images include magnetic resonance imaging, single photon emission computed tomography (CT), positron emission tomography, optical coherence tomography, confocal laser endoscopy, magnetic resonance spectroscopy, CT, X-ray, wireless capsule endoscopy, breast cancer, papanicolaou smear, hyper spectral image, and ultrasound use to diagnose different body organs and cases. Medical image analysis is appropriate environment to interact with automate intelligent system technologies. Among the intelligent systems deep learning (DL) is the modern one to manipulate medical image analysis processes and processing an image into fundamental components to extract meaningful information. The best model to establish its systems is deep convolutional neural network. This study relied on reviewing of some of these studies because of these reasons; improvements of medical imaging increase demand on automate systems of medical image analysis using DL, in most tested cases, accuracy of intelligent methods especially DL methods higher than accuracy of hand-crafted works. Furthermore, manually works need a lot of time compare to systematic diagnosis.

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