Oral Cancer Detection: Feature Extraction &amp; SVM Classification

Harnale, Shilpa; Maktedar, Dhananjay D.

doi:10.35444/ijana.2019.11036

Cited by 5 publications

(2 citation statements)

References 2 publications

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“…Shilpa Harnale et al [45] suggested the Support Vector Machine classifier for oral cancer detection. An effective algorithm for detecting lesions in the early phases and achieving high precision is the Hybrid Approach to KFCM segmentation.…”

Section: Related Workmentioning

confidence: 99%

Predicting Neurological Disorders Linked to Oral Cavity Manifestations Using an IoMT-Based Optimized Neural Networks

et al. 2020

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Anatomically, oral cavity and central nervous system have a close relationship; the mouth and face are the location for 30-40% of the body's sensory and motor nerves. The identification of orofacial manifestations of neurological disorders is usually in direct relation with the responsibilities of a dental surgeon. Therefore, familiarizing dental surgeons with theses manifestations is essential to have better recognition, diagnosis, and correct decisions upon treating their associated Neurological Disorders. These manifestations should be efficiently analyzed using novel effective techniques since their related neurological disorders need to be early identified to avoid serious consequences. Furthermore, preventive dental care for patients with neurological disorders and all kind of rehabilitative treatments necessitates well-planned and effective novel approaches. The Internet of Medical Thing (IoMT) is a relatively new technology that has revolutionized health care sector drastically since last decade. It's a combination of medical sensors and wearable devices that can connect to health care storage systems via networking technologies allowing the transfer of medical data over a secure network. The data transferred are of utmost importance in diseases diagnosis and treatment. In this paper, an IoMT-based Intelligent Guided Particle Local Search with Optimized Neural Networks (IGPLONN) approach is proposed. Firstly, dental data are collected from the International Collaboration on Cancer Reporting (ICCR) oral cavity and central nervous system. Secondly, features are extracted from data and IGPLONN algorithm is utilized to select the effective features by minimizing the feature dimension that helps improve the overall prediction rate. Finally, the obtained features are transferred to the central health application through the IoMT platform where they can be analyzed by dental practitioners for neurological disorders prediction. The hybrid optimized technique improves the overall oral-linked neurological diseases detection rate. Moreover, it efficiently manages the forecast parameters that are used to predict the dental metastasis with minimum computational complexity. The performance of the proposed system has been experimentally evaluated on MATLAB to verify its excellence. The results revealed that proposed IoMT-based IGPLONN method attains the maximum accuracy of 98.3% compared to other methods. INDEX TERMS Oral cavity, neurological disorder, central nervous system, intelligent guided particle local search algorithm, neural network, Internet of Medical Things (IoMT).

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Section: Related Workmentioning

confidence: 99%

Predicting Neurological Disorders Linked to Oral Cavity Manifestations Using an IoMT-Based Optimized Neural Networks

et al. 2020

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“…Performance metrics, including accuracy, sensitivity [15], and specificity, exhibit commendable levels, suggesting the effectiveness of the combined ResNet18-SVM methodology. Comparative analyses against existing methods underscore the potential of our approach in facilitating early and accurate oral cancer diagnosis [7,9]. The implications of automated oral cancer detection are far-reaching, with the potential to revolutionize clinical practices by enabling prompt interventions and improving patient prognosis.…”

mentioning

confidence: 99%

Automated Oral Cancer Detection using Convolutional Neural Networks and Support Vector Machines

2023

IJANSER

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Oral cancer is a significant public health concern, demanding early detection and intervention for improved patient outcomes. In this study, we propose an automated method for oral cancer detection leveraging state-of-the-art deep learning techniques. Convolutional Neural Networks (CNNs), specifically the ResNet18 architecture [3,12], are employed for feature extraction from oral images, followed by classification using Support Vector Machines (SVMs). The dataset comprises a collection of oral images encompassing various stages and types of oral cancer [17]. Our methodology involves preprocessing steps to standardize image dimensions and augment the dataset. The ResNet18 model is utilized to extract discriminative features from the images, which are subsequently fed into an SVM classifier for binary classification distinguishing between cancerous and non-cancerous oral images [2,20]. The evaluation of our proposed approach demonstrates promising results in automated oral cancer detection. Performance metrics, including accuracy, sensitivity [15], and specificity, exhibit commendable levels, suggesting the effectiveness of the combined ResNet18-SVM methodology. Comparative analyses against existing methods underscore the potential of our approach in facilitating early and accurate oral cancer diagnosis [7,9]. The implications of automated oral cancer detection are far-reaching, with the potential to revolutionize clinical practices by enabling prompt interventions and improving patient prognosis. Future research directions encompass exploring diverse CNN architectures, integrating multi-modal data sources, and refining the proposed methodology for enhanced diagnostic precision [8,14]. This study signifies a significant stride towards automated oral cancer detection, laying the groundwork for leveraging advanced deep learning techniques in the realm of medical image analysis for improved healthcare outcomes [1,5].

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A Machine Learning-Based Framework for Oral Cancer Classification Using Ant Bee Colony Optimization with Support Vector Machine Classifier

Kumari,

Mehta,

Khan

2023

2023 Second International Conference on Advances in Computational Intelligence and Communication (ICACIC)

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Oral Cancer Detection: Feature Extraction & SVM Classification

Cited by 5 publications

References 2 publications

Predicting Neurological Disorders Linked to Oral Cavity Manifestations Using an IoMT-Based Optimized Neural Networks

Predicting Neurological Disorders Linked to Oral Cavity Manifestations Using an IoMT-Based Optimized Neural Networks

Automated Oral Cancer Detection using Convolutional Neural Networks and Support Vector Machines

A Machine Learning-Based Framework for Oral Cancer Classification Using Ant Bee Colony Optimization with Support Vector Machine Classifier

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