Deep learning for liver tumour classification: enhanced loss function
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Deep learning for liver tumour classification: enhanced loss function Simranjeet Randhawa 1 & Abeer Alsadoon 1 Ahmed Dawoud 1 & Ahmad Alrubaie 2
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& P.W.C. Prasad & Thair Al-Dala’in &
Received: 30 May 2020 / Revised: 29 August 2020 / Accepted: 16 September 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract
Background and Aim: deep learning has not been successfully implemented in liver tumour feature extraction and classification using computer-aided diagnosis. This study aims to enhance classification accuracy and improves the processing time to better differentiate tumour types. Methodology: This study proposed a hybrid model, which combines the regularization function with the current loss function for the support vector machine (SVM) classifier. Regularization function is used for prioritizing image classes before feeding it to the linear mapping. The proposed model consists of the region growing algorithm to get the region-of-interest (ROI), and Weiner filtering algorithm for image enhancement and noise removal. The gray level co-occurrence matrix (GLCM) was performed to extract the feature from the image. The extracted feature then fed to SVM classifier using selected feature vectors to classify the affected region and neglecting the unwanted areas. Results: classification accuracy was calculated using probability score, and the processing time was calculated based on the total execution time. The proposed system was able to achieve an average classification accuracy of 98.9%, which is about 2–3% higher than the current system. The results showed that 12 ms reduced the processing time on average. Conclusion: The proposed system focused on improving feature extraction and classification for different types of tumours from the MRI images. The study solved the problem in linear mapping of support vector machine and enhanced the classification accuracy and the processing time of early diagnosis of three different types of tumours in liver MRI images. Keywords Deep learning . Computer-aided diagnosis . Spinal metastases . Liver cancer . Watershed transform . Gray level co-occurrence matrix . Support vector matrix
* Abeer Alsadoon [email protected]
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School of Computing and Mathematics, Charles Sturt University, Sydney, Australia
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Faculty of Medicine, University of New South Wales, Sydney, Australia
Multimedia Tools and Applications
1 Introduction Computer-aided diagnosis (CAD) systems have been widely used in liver surgery and classification of tumours in the liver. CAD can help to early detection of cancer and provides a better chance of treatment [10]. Previously the classification was performed manually by comparing the MRI images with stored image data. This manual process had a lack of accuracy, efficiency, and takes more decision-making time, which delayed liver disease diagnosis. In recent years, many image segmentation techniques have been introduced, such as watershed segmentation, spatial gray level decision matrix (SGLDM) [28]. CAD has been used fo
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