Towards Better Diagnosis of Lung Cancer: Associative and Locative Measure


Towards Better Diagnosis of Lung Cancer: Associative and Locative Measure – The main task of lung cancer is to assess the prognosis of patients who have recently developed a new lung cancer. In this paper, a novel method for lung cancer classification based on an unsupervised learning algorithm is proposed. The method requires no human annotation, and it exploits knowledge of existing lung cancer classification datasets to generate the knowledge of patients. In this paper, we propose a dataset, named LungNess, for this purpose. The dataset contains different cancer classes and different lung tumour types. We then classify the tumour types according to their proximity to the lung cancer and predict that within a certain time period, the classification error will be near the maximum. We then use this dataset to perform lung cancer classification, in a manner which results in a smaller classification error than the previously proposed method. The proposed method is developed to provide more robust classification accuracy. The proposed method is illustrated in a lung cancer classification dataset.

We present a simple yet very effective method for extracting a single opinion from a large amount of reviews, which can reduce the number of possible ratings in the process. Our method uses a sparse representation of the review information to approximate a vector of values to be associated with each review, given by the review’s label. A supervised learning algorithm is then adapted to extract this knowledge in order to improve the classification performance. Experiments have been performed on different datasets, which show that our approach significantly outperforms the state-of-the-art baselines for predicting reviews. Furthermore, despite the different data sets, we can find the best ranking score for our algorithm, which we show is 98.31% accuracy for the MNIST dataset.

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Towards Better Diagnosis of Lung Cancer: Associative and Locative Measure

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  • Adaptive Stochastic Learning

    Exploring the structure of object reviews using gradient assisted binary hashingWe present a simple yet very effective method for extracting a single opinion from a large amount of reviews, which can reduce the number of possible ratings in the process. Our method uses a sparse representation of the review information to approximate a vector of values to be associated with each review, given by the review’s label. A supervised learning algorithm is then adapted to extract this knowledge in order to improve the classification performance. Experiments have been performed on different datasets, which show that our approach significantly outperforms the state-of-the-art baselines for predicting reviews. Furthermore, despite the different data sets, we can find the best ranking score for our algorithm, which we show is 98.31% accuracy for the MNIST dataset.


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