Fuzzy convex set-based pattern classification for analysis of mammographic microcalcifications

✍ Scribed by Wojciech M. Grohman; Atam P. Dhawan

• Publisher: Elsevier Science
• Year: 2001
• Tongue: English
• Weight: 468 KB
• Volume: 34
• Category: Article
• ISSN: 0031-3203
• DOI: 10.1016/s0031-3203(00)00085-6

✦ Synopsis

There are many di!erent criteria for the comparative analysis of pattern classi"ers. They include generalization ability, computational complexity and understanding of the feature space. In some applications such as the medical diagnostic systems it is crucial to use reliable tools, whose behavior is always predictable, so that the risk of misdiagnosis is minimized. In such applications the use of the popular feedforward backpropagation (BP) neural network algorithm can be seen as questionable. This is because it is not inherent for the backpropagation method to analyze the problem's feature space during training, which can sometimes result in inadequate decision surfaces. A novel convex-set-based neuro-fuzzy algorithm for classi"cation of di$cult-to-diagnose instances of breast cancer is described in this paper. With its structural approach to feature space the new method o!ers rational advantages over the backpropagation algorithm. The classi"cation performance, computational and structural e$ciencies are analyzed and compared with that of the BP network. A 20-dimensional set of `di$cult-to-diagnosea mammographic microcalci"cations was used to evaluate the neuro-fuzzy pattern classi"er (NFPC) and the BP methods. In order to evaluate the learning ability of both methods, the relative size of training sets was varied from 40 to 90%. The comparative results obtained using receiver operating characteristic (ROC) analysis show that the ability of the convex-set-based method to infer knowledge was better than that of backpropagation in all of the tests performed, making it more suitable for use in real diagnostic systems.