Machine learning for evolutive lymphoma and residual masses recognition in whole body diffusion weighted magnetic resonance images. (September 2021)

• Record Type: Journal Article
• Authors: Ferjaoui, Radhia; Cherni, Mohamed Ali; Boujnah, Sana; Kraiem, Nour El Houda; Kraiem, Tarek
• Is Part Of: Computer methods and programs in biomedicine. Volume 209(2021)
• Publication Date: 2021-09
• Subjects: Evolutive lymphoma -- Residual masses -- Machine learning -- Deep learning -- Back-propagation artificial neural network -- Support vector machine -- K-nearest neighbours -- Random forest -- Relevance vectors machine (RVM) -- Convolutional neural network (CNN); Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2021.106320
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: The complementarity of the functional and anatomical criteria of DW-MR images through a fusion step based on DWT. Automatic segmentation of the lesions, their localization, their enumeration and the generation of the parametric ADC map. The analysis of the heterogeneous lesions to select the solid part from the whole lesion based on the Fast Fuzzy C-means. The mahine learning is based on the comparison of 5 algorithms: ANN, SVM, KNN, RF, and RVM. And compared to deep learning. Application of the SFS, entropy, symmetric uncertainty and gain ratio on 72 extracted features to obtain the most significant. Abstract: Background: After the treatment of the patients with malignant lymphoma, there may persist lesions that must be labeled either as evolutive lymphoma requiring new treatments or as residual masses. We present in this work, a machine learning-based computer-aided diagnosis (CAD) applied to whole-body diffusion-weighted magnetic resonance images. Methods: The database consists of a total of 1005 MRI images with evolutive lymphoma and residual masses. More specifically, we propose a novel approach that leverages: (1)-The complementarity of the functional and anatomical criteria of MRI images through a fusion step based on the discrete wavelet transforms (DWT). (2)- The automatic segmentation of the lesions, their localization, and their enumeration using the Chan-Vese algorithm. (3)- The generation of the parametric image which contains the apparent diffusion coefficient value named ADC map. (4)- The features selection through the application of the sequential forward selection (SFS), Entropy, Symmetric uncertainty and Gain Ratio algorithm on 72 extracted features. (5)- The classification of the lesions by applying five well known supervised machine learning classification algorithms: the back-propagation artificial neural network (ANN), the support vector machine (SVM), the K-nearest neighbours (K-NN), Relevance Vectors Machine (RVM), and the random forest (RF) compared to deep learning based on convolutional neural network (CNN). Moreover, this study is achieved with an evaluation of the classification using 335 DW-MR images where 80% of them are used for the training and the remaining 20% for the test. Results: The obtained accuracy for the five classifiers recorded a slight superiority to the proposed method based on the back-propagation 3-9-1 ANN model which reaches 96, 5%. In addition, we compared the proposed method to five other works from the literature. The proposed method gives much better results in terms of SE, SP, accuracy, F 1 -measure, and geometric-mean which reaches respectively 96.4%, 90.9%, 95.5%, 0.97, and 91.61%. Conclusions: Our initial results suggest that Combining functional, anatomical, and morphological features of ROI's have very good accuracy (97.01%) for evolutive lymphoma and residual masses recognition when we based on the new proposed approach using the back-propagation 3-9-1 ANN model. Proposed method based on machine learning gives less than Deep learning CNN, which is 98.5%.