[PDF] Top 20 A comparative study of algorithms used for leukemia detection

... S.Jagadeesh , Dr.E.Nagabhooshanam , Dr.S.Venkatachalam proposed segmentation of bone marrow image aspirate using watershed algorithm[16]. T. Markiewicz, S. Osowski, B., Marianska, L., Moszczynski use Support Vector ... See full document

... had a good accuracy. The interesting thing comes with boosting-GNB. For S4, the MCC scores are near zero which means the prediction is no better than ran- dom. Also, it shows around 0.16 for S10 and S11, which are ... See full document

... boundary detection is a fundamental step for content based video ...recent algorithms designed explicitly to detect specific complex editing operations such as fades and dissolves are taken into account, ... See full document

... a) Roberts Cross-Gradient Operator: It is used in image processing and computer vision for edge detection. It approximates the gradient of an image by discrete differentiation. Discrete differentiation is ... See full document

... The detection of the tumor by a neural network have been studied for several locations of the transmitter antenna from 0 ◦ to 90 ◦ with a step of 15 ◦ ...Two algorithms have been used for the ... See full document

... was used for training and testing for SVM classifier which created an ensemble of classification ...authors used pasting vote technique to aggregate this data using two different sum ...were used in ... See full document

... commonly used Artificial Intelligence algorithms such as Heuristics, A*algorithm, KNN, Linear ...AI algorithms thus giving the reader an idea about which algorithm to apply to which AI ... See full document

... a comparative study on various edge detection techniques which gives a way for selecting one suitable technique for identifying the species of ...Edge detection is one of the mostly ... See full document

... for detection of the CKD into two classes as ckd or ...A comparative analysis among the entire rule-based algorithms shows that DTNB ...this study concludes that among rule-based learners DTNB ... See full document

... often used in registry-based studies, would thus include half of the missed AMIs, ie, only very few true events would be overlooked by a use of combined end point and thereby improve ... See full document

... We get the skin tone information by extracting the hue from the frame and converting it to HSV color space. With the skin tone as the selected parameter, we use the „vision.HistogramBasedTracker‟ for tracking. The ... See full document

... they used various image processing steps like noise removal from input images using median filter, convert colored image to gray scale image, for enhancement of the image histogram equalization was applied, K-Mean ... See full document

... R-CNN works really well, but is really quite slow for a few simple reasons It requires a forward pass of the CNN for every single region proposal for every single image. It has to train three different models separately ... See full document

... main algorithms have been compared based on certain important factors or parameters which are necessary to be considered at the time of performing the clustering ...datasets used and the number of datasets ... See full document

... Nezami, Omid Mohamad, et al. [4] This paper presents a deep learning model to improve engagement recognition from images using pre-training on available basic facial expression data, before training on specialised ... See full document

... The Sobel edge detection technique is similar to that of the Robert Cross algorithm. Despite the design of Sobel and Robert are common, the main difference is the kernels that each uses to obtain the image is ... See full document

... illegally used and distributed through high-speed digital ...tampering detection algorithms in active and passive methods became a significant research ...paper, comparative analysis on the ... See full document

... a study on each of them. Data mining can be used in a wide area that integrates techniques from various fields including machine learning, Network intrusion detection, spam filtering, artificial ... See full document

... Fuzzy clustering is a powerful unsupervised method for the analysis of data and construction of models. In many situations, fuzzy clustering is more natural than hard clustering. Objects on the boundaries between several ... See full document

... the used matrix , all the distribution coefficients are normalized by pairs , the resulting data (Squalane, DPMS) (Squalane, PMHS) (PMHS, DPMS) are represented in 2D ... See full document