基于元启发式多目标方法的卷积神经网络膀胱癌诊断模型选择

I. Lorencin, Klara Smolić, D. Markić, J. Španjol
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引用次数: 0

摘要

膀胱癌是泌尿道最常见的恶性肿瘤之一。它具有高转移潜力和高复发率的特点,这大大增加了诊断和治疗的复杂性。为了提高诊断过程的准确性,介绍了基于人工智能的诊断算法。本文提出了基于多目标方法的卷积神经网络(CNN)模型的选择原则,以最大限度地提高分类和泛化性能。模型选择在AlexNet和VGG-16两种标准CNN架构上进行。分类性能通过使用ROC分析和得到的AUC值来衡量。另一方面,泛化性能通过使用5倍交叉验证程序进行评估。利用这两个指标,设计了用于元启发式算法的多目标适应度函数。采用遗传算法(GA)和离散粒子群算法(D-PS)进行多目标搜索。从得到的结果可以看出,这种方法使得CNN模型具有较高的分类和泛化性能。当使用基于ga的方法时,适应度值最高可达0.97。另一方面,使用D-PS方法获得的适应度值高达0.99,表明该方法为模型提供了更高的分类和泛化性能。
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A META-HEURISTIC MULTI-OBJECTIVE APPROACH TO THE MODEL SELECTION OF CONVOLUTION NEURAL NETWORKS FOR URINARY BLADDER CANCER DIAGNOSIS
Bladder cancer is one of the most common malignancies of the urinary tract. It is characterized by high metastatic potential and a high recurrence rate, which significantly complicates diagnosis and treatment. In order to increase the accuracy of the diagnostic procedure, algorithms based on artificial intelligence are introduced. This paper presents the principle of selection of convolutional neural network (CNN) models based on a multi-objective approach that maximizes classification and generalization performance. Model selection is performed on two standard CNN architectures, AlexNet and VGG-16. Classification performances are measured by using ROC analysis and the resulting AUC value. On the other hand, generalization performances are evaluated by using a 5-fold cross-validation procedure. By using these two metrics, a multi-objective fitness function, used in meta-heuristic algorithms, is designed. The multi-objective search was performed using a Genetic algorithm (GA) and a Discrete Particle Swarm (D-PS) algorithm. From obtained results, it can be noticed that such an approach has resulted in CNN models that are defined with high classification and generalization performances. When a GA-based approach is used, fitness values up to 0.97 are achieved. On the other hand, by using the D-PS approach, fitness values up to 0.99 are achieved pointing towards the conclusion that such an approach has provided models with higher classification and generalization performances.
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