A neuro-heuristic approach for recognition of lung diseases from X-ray images
یک روش عصبی و اکتشافی برای شناخت بیماری های ریه از تصاویر اشعه ایکس-2019
Background and objective: The X-ray screening is one of the most popular methodologies in detection of respiratory system diseases. Chest organs are screened on the film or digital file which go to the doctor for evaluation. However, the analysis of x-ray images requires much experience and time. Clinical decision support is very important for medical examinations. The use of Computational Intelligence can simulate the evaluation and decision processes of a medical expert. We propose a method to provide a decision support for the doctor in order to help to consult each case faster and more precisely. Methods: We use image descriptors based on the spatial distribution of Hue, Saturation and Brightness values in x-ray images, and a neural network co-working with heuristic algorithms (Moth-Flame, Ant Lion) to detect degenerated lung tissues in x-ray image. The neural network evaluates the image and if the possibility of a respiratory disease is detected, the heuristic method identifies the degenerated tissues in the x-ray image in detail based on the use of the proposed fitness function. Results: The average accuracy is 79.06% in pre-detection stage, similarly the sensitivity and the specificity averaged for three pre-classified diseases are 84.22% and 66.7%, respectively. The misclassification errors are 3.23% for false positives and 3.76% for false negatives. Conclusions: The proposed neuro-heuristic approach addresses small changes in the structure of lung tissues, which appear in pneumonia, sarcoidosis or cancer and some consequences that may appear after the treatment. The results show high potential of the newly proposed method. Additionally, the method is flexible and has low computational burden.
Keywords: Medical image processing | Clinical decision support | Neural networks | Heuristic methods
Heuristic approximation and computational algorithms for closed networks: A case study in open-pit mining
تقریب اکتشافی و الگوریتم های محاسباتی برای شبکه های بسته: یک مطالعه موردی در واکاوی میدان - آزاد-2018
We investigate a fundamental model from open-pit mining which is a cyclic system consisting of an (unreliable) shovel, trucks travelling loaded, unloading facility, and trucks travelling back empty. The interaction of these subsystems determines the mean number of trucks loaded per time unit — the capacity of the shovel, which is a fundamental quantity of interest. To determine this capacity we need the stationary probability that the shovel is idle. Because an exact analysis of the performance of the system is out of reach, besides of simulations there are various approximation algorithms proposed in the literature, which stem from computer science and can be characterized as general purpose algorithms. We propose for solving the special problem under mining conditions an extremely simple alternative algorithm. Comparison with several general purpose algorithms shows that for realistic situations in the open-pit mining application the special algorithm outperforms the precision of general purpose algorithms. This holds even if the general purpose algorithms incorporate more details of the underlying models than our simple algorithm, which is based on a strongly reduced model. The comparison and assessment is done with extensive simulations on a level of detail which the general purpose algorithms are able to cover. We discuss the application of our proposed algorithms to other applications. It turns out that our algorithms are analogues to Norton’s Theorem for a large class of general transportation systems.
keywords: Mining |Queues |Transport |Algorithms |Heuristic methods |Long-run idle times