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%.

背景：恶性淋巴瘤患者治疗后，可能存在持续的病变，必须标记为需要新治疗的演进性淋巴瘤或残留肿块。我们在这项工作中提出了一种基于机器学习的计算机辅助诊断 (CAD)，应用于全身扩散加权磁共振图像。

方法：该数据库由总共 1005 张 MRI 图像组成，其中包含演进性淋巴瘤和残留肿块。更具体地说，我们提出了一种新颖的方法，该方法利用： (1)-通过基于离散小波变换 (DWT) 的融合步骤，MRI 图像的功能和解剖标准的互补性。(2)- 使用 Chan-Vese 算法自动分割病变、定位和枚举。(3)- 包含表观扩散系数值的参数图像的生成，称为ADC图。(4)- 通过对 72 个提取特征应用顺序前向选择 (SFS)、熵、对称不确定性和增益比算法进行特征选择。(5)- 通过应用五种众所周知的监督机器学习分类算法对病变进行分类：反向传播人工神经网络 (ANN)、支持向量机 (SVM)、K-最近邻 (K-NN)、相关向量机 (RVM) 和随机森林 (RF) 与基于深度学习的相比卷积神经网络 (CNN)。此外，这项研究是通过使用 335 张 DW-MR 图像对分类进行评估来实现的，其中 80% 用于训练，其余 20% 用于测试。

结果：获得的五个分类器的准确率略高于基于反向传播 3-9-1 ANN 模型的方法，达到 96.5%。此外，我们将所提出的方法与文献中的其他五部作品进行了比较。所提出的方法在 SE、SP、准确性、$F_1$-measure 和几何平均值分别达到 96.4%、90.9%、95.5%、0.97 和 91.61%。

结论：我们的初步结果表明，当我们基于使用反向传播 3-9-1 的新建议方法时，结合 ROI 的功能、解剖和形态特征对进化性淋巴瘤和残留肿块的识别具有非常好的准确度 (97.01%)神经网络模型。提出的基于机器学习的方法给出的结果低于深度学习 CNN，为 98.5%。