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Classifying medical notes into standard disease codes using Machine Learning
We investigate the automatic classification of patient discharge notes i...
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Deep learning trends for focal brain pathology segmentation in MRI
Segmentation of focal (localized) brain pathologies such as brain tumors...
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Hybrid Machine Learning Models of Classifying Residential Requests for Smart Dispatching
This paper presents a hybrid machine learning method of classifying resi...
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Image based cellular contractile force evaluation with small-world network inspired CNN: SW-UNet
We propose an image-based cellular contractile force evaluation method u...
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An Improvement of Data Classification Using Random Multimodel Deep Learning (RMDL)
The exponential growth in the number of complex datasets every year requ...
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On the dissection of degenerate cosmologies with machine learning
Based on the DUSTGRAIN-pathfinder suite of simulations, we investigate o...
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Metaheuristic Algorithms for Convolution Neural Network
A typical modern optimization technique is usually either heuristic or m...
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Approaching Bio Cellular Classification for Malaria Infected Cells Using Machine Learning and then Deep Learning to compare analyze K-Nearest Neighbours and Deep CNNs
Malaria is a deadly disease which claims the lives of hundreds of thousands of people every year. Computational methods have been proven to be useful in the medical industry by providing effective means of classification of diagnostic imaging and disease identification. This paper examines different machine learning methods in the context of classifying the presence of malaria in cell images. Numerous machine learning methods can be applied to the same problem; the question of whether one machine learning method is better suited to a problem relies heavily on the problem itself and the implementation of a model. In particular, convolutional neural networks and k nearest neighbours are both analyzed and contrasted in regards to their application to classifying the presence of malaria and each models empirical performance. Here, we implement two models of classification; a convolutional neural network, and the k nearest neighbours algorithm. These two algorithms are compared based on validation accuracy. For our implementation, CNN (95 than kNN (75
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