Deep learning neural network for texture feature extraction in oral cancer: enhanced loss function
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Deep learning neural network for texture feature extraction in oral cancer: enhanced loss function Bishal Bhandari 1 & Abeer Alsadoon 1 Sami Haddad 3,4
1
2
& P. W. C. Prasad & Salma Abdullah &
Received: 27 October 2019 / Revised: 28 June 2020 / Accepted: 16 July 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
The use of a binary classifier like the sigmoid function and loss functions reduces the accuracy of deep learning algorithms. This research aims to increase the accuracy of detecting and classifying oral tumours within a reduced processing time. The proposed system consists of a Convolutional neural network with a modified loss function to minimise the error in predicting and classifying oral tumours by reducing the overfitting of the data and supporting multi-class classification. The proposed solution was tested on data samples from multiple datasets with four kinds of oral tumours. The averages of the different accuracy values and processing times were calculated to derive the overall accuracy. Based on the obtained results, the proposed solution achieved an overall accuracy of 96.5%, which was almost 2.0% higher than the state-of-the-art solution with 94.5% accuracy. Similarly, the processing time has been reduced by 30–40 milliseconds against the state-of-the-art solution. The proposed system is focused on detecting oral tumours in the given magnetic resonance imaging (MRI) scan and classifying whether the tumours are benign or malignant. This study solves the issue of over fitting data during the training of neural networks and provides a method for multi-class classification. Keywords Deep learning . Convolutional neural network (CNN) . Oral tumor . Loss function . Region of interest (ROI)
* Abeer Alsadoon [email protected]
1
School of Computing and Mathematics, Charles Sturt University, Sydney Campus, Sydney, Australia
2
Department of Computer Engineering, University of Technology, Baghdad, Iraq
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Department of Oral and Maxillofacial Services, Greater Western Sydney Area Health Services, Mount Druitt, Australia
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Department of Oral and Maxillofacial Services, Central Coast Area Health, Gosford, Australia
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1 Introduction Oral cancer is a complex wide spread cancer, which has high severity. Using advanced technology and deep learning algorithm early detection and classification are made possible. Medical imaging technique, computer-aided diagnosis and detection can make potential changes in cancer treatment, can now be detected in the early stages by analysing computed tomography (CT) scans, magnetic resonance imaging (MRI) scans, and X-ray images [16]. This makes the anatomical analysis of the oral cavity easier and enables the accurate extraction of the normal region from the tumour-prone regions. During the tumour extraction stage, the image needs to be applied with various segmentation approaches to separate the normal regions and cancer-prone regions. This fails to segment the image properly and increases th
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