Name
Abstract | ||
|---|---|---|
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 64x64 pixels, which is an improvement compared with our previous works. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1142/S0218001421600077 | INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE |
Keywords | DocType | Volume |
Medical image analysis, ontology, deep learning, HCC, multi-label, CNN, MRI classification | Journal | 35 |
Issue | ISSN | Citations |
12 | 0218-0014 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rim Messaoudi | 1 | 0 | 2.03 |
| Faouzi Jaziri | 2 | 5 | 3.53 |
| Achraf Mtibaa | 3 | 40 | 11.23 |
| Faïez Gargouri | 4 | 244 | 92.29 |
| Antoine Vacavant | 5 | 0 | 0.34 |