A Novel Public Database of Lymphoma for Whole Body Diffusion-Weighted MRI

Abstract

Lymphoma affects the human lymphatic system, it has become one of the leading causes of patient deaths. Hence, accurate diagnosis is essential to help doctors prescribe a suitable treatment. In this work, we propose a new database that contains 50 volunteer patients treated for lymphoma on Whole Body (head/neck, chest, abdomen, and pelvis regions). These collected databases contain some MRI sequences and medical information of patients which tells whether that lesion is evolutive lymphoma or residual masses. It is mainly composed of 100000 images on axial, coronal, and sagittal plans. To highlight the utility of this dataset, we used a computer-aided diagnosis (CAD) system to recognize evolutive lymphoma from residual masses. This recognition is very important from a medical point of view, as it helps identify patients who may need additional therapy. After successful steps of database preparation, features extraction and selection, we evaluated several Machine Learning models such as Random Forest, Decision Tree, Naive Bayes, Extreme Gradient Boost, Logistic Regression, K-Nearest Neighbors (K-NN), and Support Vector Machine (SVM). The metrics of Accuracy, Precision, Sensitivity, Specificity, confusion matrix, Positive Predictive Value, Negative Predictive Value, Fl-score, missed classification, and Recall was measured to evaluate our CAD system based on AI models. The best obtained results reach 95% accuracy for SVM with RBF kernel. In addition, the proposed approach was compared to three literature works, and it gives much better results that can correctly recognize more than 64 lesions out of 67 cases. Interested researchers could contact the author to acquire the database.