This paper present Extreme Learning Machine to classify lung cancer nodules. Lung cancer is a type of lung disease that requires fast and specified treatment. Skills, facilities and multidisciplinary approach are required for diagnosing lung cancer. The use of Computed Tomography (CT) to detect lung cancer can reduce the number of deaths from lung cancer, but it increases the workload of the radiologist because CT screening process produces many medical images. Computer systems become one of the potential solutions to help radiologists solve the problem. Extreme Learning Machine is an algorithm that able to provide good generalization at fast learning time which is essential to help radiologists in analyzing lung cancer nodules images. In this paper, there were 877 nodules extracted from LIDC-IDRI dataset. All nodules used in this experiment consist of lung cancer nodules that diagnosed to four different level of malignancy and annotated by up-to four different radiologists. The result shows Extreme Learning Machine achieve 85.17%, 85.58% and 84.87% in accuracy and Matthew Correlation Coefficient 0.755, 0.762 and 0.749 using Hardlimit, Radial basis Function and Triangular Basis function, respectively.
Motorcycles are the most significant contributor to the vehicle numbers in Indonesia, about 81% of all vehicles in the country. In addition, the growth of modified motorcycles has also increased in several areas, particularly remote places. Many studies have been conducted for detecting vehicles. However, most vehicle detection studies were conducted to detect cars or four-wheeled vehicles, and only a few studies were done to detect motorcycles. Further problems increase if the system is implemented in remote areas with limited electricity power resources that need low-cost budget specification computation. This study detects and calculates the number of motor vehicles and modified motorcycles passed on a highway from video data.
It proposed Machine Learning instead of Deep Learning to suit the low computational video in remote areas. Computer visionbased methods used in the prediction are optical flow andHistogram Oriented Gradient (HOG) + Support Vector Machine (SVM). Five videos were used in the system testing, taken from the roadsides using a static camera with a resolution of 160x112 pixels at ±135º angle. This research showed that the accuracy of motorcycles and modified motorcycles detection and calculation systems using the HOG + SVM method is higher than the optical flow method. The average accuracy of HOG + SVM for motorcycles and modified motorcycles is 89.70% and 95.16%, respectively.
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