Small Lung Nodules Detection Based on Fuzzy-Logic and Probabilistic Neural Network With Bioinspired Reinforcement Learning

Abstract

Internal organs, like lungs, are very often examined by the use of screening methods. For this purpose we present an evaluation model based on a composition of fuzzy system combined with a neural network. The input image is evaluated by means of custom rules which use type-1 fuzzy membership functions. The results are forwarded to a neural network for final evaluation. Our model was validated by using X ray images with lung nodules. The results shows the high performances of our approach with sensitivity and specificity reaching almost 95% and 90% respectively, with an accuracy of 92.56%. The new methodology lower considerably the computational demands and increases detection performances.

Breast Cancer–Detection System Using PCA, Multilayer Perceptron, Transfer Learning, and Support Vector Machine

“…An evaluation model based on the combination of a fuzzy system and neural networks was used for screening and testing through image input and selfdefined fuzzy membership functions. Then, neural networks were employed to perform a final evaluation [21]. The regional reconstruction of computed tomography scan images was improved using a self-adaptive variation of the partial differential equation model [22].…”

Semantic Segmentation of Conjunctiva Region for Non-Invasive Anemia Detection Applications

“…In recent years, researchers focusing towards deep learning-based image segmentation due to the availability of online resource materials, easy accessibility of high computational power, availability of computer vision and other supporting libraries, and the potential of Convolution Neural Network architecture in obtaining effective segmentation results [35][36][37][38][39]. In the literature, an interesting application involving neural networks and more broadly computer vision techniques is microscopy image analysis [40,41], the analogy with our domain stands in various environmental factors which can lead to a false interpretation of the results by human professionals.…”

On Granular Rough Computing: Handling Missing Values by Means of Homogeneous Granulation

“…Being in the context of rough mereology was proposed by L. Polkowski and A.Skowron, approximation spaces by A. Skowron and J. Stepaniuk [8,9], and logic for approximate reasoning by L.Polkowski and M. Semeniuk-Polkowska [10], and Qing Liu [11]. Examples of interesting studies from recent years can be found in [12][13][14][15][16][17][18].…”