Liver cancer is the third deadliest cancer in the world. It characterizes a malignant tumor that develops through liver cells. The hepatocellular carcinoma (HCC) is one of these tumors. Hepatic primary cancer is the leading cause of cancer deaths. This article deals with the diagnostic process of liver cancers. In order to analyze a large mass of medical data, ontologies are effective; they are efficient to improve medical image analysis used to detect different tumors and other liver lesions. We are interested in the HCC. Hence, the main purpose of this paper is to offer a new ontology-based approach modeling HCC tumors by focusing on two major aspects: the first focuses on tumor detection in medical imaging, and the second focuses on its staging by applying different classification systems. We implemented our approach in Java using Jena API. Also, we developed a prototype OntHCC by the use of semantic aspects and reasoning rules to validate our work. To show the efficiency of our work, we tested the proposed approach on real datasets. The obtained results have showed a reliable system with high accuracies of recall (76%), precision (85%), and F-measure (80%).
Reading and interpreting the medical image still remains the most challenging task in radiology. Through the important achievement of deep Convolutional Neural Networks (CNN) in the context of medical image classification, various clinical applications have been provided to detect lesions from Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scans. In the diagnosis process for the liver cancer from Dynamic Contrast-Enhanced MRI (DCE-MRI), radiologists consider three phases during contrast injection: before injection, arterial phase, and portal phase for instance. Even if the contrast agent helps in enhancing the tumoral tissues, the diagnosis may be very difficult due to the possible low contrast and pathological tissues surrounding the tumors (cirrhosis). Alongside, in the medical field, ontologies have proven their effectiveness to solve several clinical problems such as offering shareable terminologies, vocabularies, and databases. In this article, we propose a multi-label CNN classification approach based on a parallel preprocessing algorithm. This algorithm is an extension of our previous work cited in the International Conference on Pattern Recognition and Artificial Intelligence (ICPRAI) 2020. The aim of our approach is to ameliorate the detection of HCC lesions and to extract more information about the detected tumor such as the stage, the localization, the size, and the type thanks to the use of ontologies. Moreover, the integration of such information has improved the detection process. In fact, experiments conducted by testing with real patient cases have shown that the proposed approach reached an accuracy of 93% using MRI patches of [Formula: see text] pixels, which is an improvement compared with our previous works.
Ontology, as a useful knowledge engineering technique, has been widely used for reducing ambiguity and helping with information sharing. It is considered originally to be clear, comprehensive and with welldefined format. It characterizes several domains purposes description through structured and formalized languages. In various areas of research, it has become a significant way to realize successful and powerful accomplishments. Actually, medical ontologies were turned into an efficient application in medical domains. They also become a relevant approach to process large medical data volumes. Consequently, they are behaving as a support decision system in some cases. Also, they ensure diagnosis process acceleration and assistance. Additionally, they have been integrated especially to represent human healthcare concepts. For that reason, plenty of research works applied ontologies to design and treat liver diseases. In this article, we present a general overview of medical ontologies to stand for this type of disease. We expose and discuss these works in details by a complete comparison. Also, we show their performance to arrange clinical data and extract results.
This work proposes a deep learning algorithm based on the Convolutional Neural Network (CNN) architecture to detect HepatoCellular Carcinoma (HCC) from liver DCE-MRI (Dynamic Contrast-Enhanced MRI) sequences. The Deep Learning technique is an artificial intelligence technique (AI) that tries to imitate the human brain work in the training data and creating models used for decision. Actually, it is widely used for various clinical issues. To diagnose HCC, radiologists consider three different phases during contrast injection (before injection; arterial phase; portal phase for instance). This paper presents an approach that offers a parallel preprocessing algorithm. It allows HCC detection and localization in MRI images via a CNN algorithm. The created CNN model reached an accuracy level of 90% in both arterial and portal phases using MRI patches of 64×64 pixels. We mention also its ability to decrease false detection comparing with our previous works. The obtained good accuracy is considered to be ameliorated in our future works.
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