Deep learning for liver tumour classification: enhanced loss function

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 st...

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Bibliographic Details
Published in:	Multimedia tools and applications Vol. 80; no. 3; pp. 4729 - 4750
Main Authors:	Randhawa, Simranjeet, Alsadoon, Abeer, Prasad, P.W.C., Al-Dala’in, Thair, Dawoud, Ahmed, Alrubaie, Ahmad
Format:	Journal Article
Language:	English
Published:	New York Springer US 2021 Springer Nature B.V
Subjects:	Accuracy Algorithms Classification Classifiers Computer Communication Networks Computer Science Current loss Data Structures and Information Theory Deep learning Diagnosis Feature extraction Image classification Image enhancement Image filters Liver Liver cancer Machine learning Magnetic resonance imaging Mapping Medical imaging Multimedia Information Systems Regularization Special Purpose and Application-Based Systems Support vector machines Tumors Deep learning Liver cancer Gray level co-occurrence matrix Support vector matrix Watershed transform Spinal metastases Computer-aided diagnosis
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Summary:	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.
ISSN:	1380-7501 1573-7721
DOI:	10.1007/s11042-020-09900-8