CANCER DETECTION USING CONVOLUTIONAL NEURAL NETWORK

Author Reg. ID: TIJER_151489

Published Paper Id: TIJER2403093

Published In: Volume 11 Issue 3, March-2024

Abstract: An artificial intelligence algorithm known as an oral, or convolutional neural network, may examine medical photos to find oral cancer. It uses several layers of convolution pooling to identify patterns and features in the images. This aids CNN in categorizing and locating potentially malignant oral cavity regions. With 177,384 deaths from oral cancer in 2018, oral cancer is a serious worldwide health concern that is especially common in low- and middle-income nations. The study employed a Convolution Neural Network (CNN) for classifying oral cancer cases. Their findings suggested that CNN performs better in the detection of oral cancer compared to other methods[4].. The data used here is basically a primary datasets. Warin, Kritsasith, et al in his research paper he has tried to explain AI-based CNNs detect oral lesions, aiding early oral cancer detection, potentially improving diagnostic rates[2]. In particular, Convolutional Neural Networks (CNNs) have demonstrated encouraging performance in a range of medical image processing applications. CNNs have been extensively employed in automated whole-slide analysis for the purpose of cancer identification. Most often, a wide range of parameters, such as sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC-ROC), are used in the proposed CNN-based system.

Keywords: Convolutional Neural Network, Detection of oral cancer, Image processing techniques.

Downloads: 00028

Page No: a687-a692

Country: mumbai, maharashtra, India

Research Area: Humanities All

Published Paper URL: https://tijer.org/TIJER/viewpaperforall?paper=TIJER2403093

Published Paper PDF: https://tijer.org/TIJER/papers/TIJER2403093

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