UMAP guided topological analysis of transcriptomic data for cancer subtyping

Chachoo, Manzoor Ahmad

doi:10.1007/s41870-022-01048-y

Cited by 2 publications

(1 citation statement)

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“…The induction of dictionary learning in the approach has improved its performance over some of the related techniques used for the classification of medical patterns. While others [ 29 ] have used clustering methodology by leveraging transcriptomic data for sub-typing cancer patients and is based on a non-linear dimensionality reduction technique called uniform manifold approximation and projection (UMAP) and a tool from algebraic topology called mapper. Inspired by the success of hybrid and ensemble methods for developing enhanced prediction methods, we considered ensemble approach for classification of chest X-ray images.…”

Section: Related Workmentioning

confidence: 99%

Weighted ensemble model for image classification

Iqball

Wani

2023

Int. j. inf. tecnol.

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The Deep Convolutional Neural Network (DCNN) classification models are being tremendously used across many research fields including medical science for image classification. The accuracy of the model and reliability on the results of the model are the key attributes which determine whether a particular model should be used for a specific application or not. A highly accurate model is always desirable for all applications of machine learning as well as deep learning. This paper presents a DCNN based heterogeneous ensemble approach where all DCNN models can be trained on a single dataset and each model can contribute of towards the final output of the ensemble model. The contribution of each model is weighted according to its individual accuracy on the given dataset. Models with higher accuracy has higher contribution in the final output of ensemble model, whereas the models with lower accuracy has lower contribution. This approach, when tested on two different X-ray images datasets of Covid-19, has confirmed the significant increase in 3-class accuracy as compared to the models in literature.

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Section: Related Workmentioning

confidence: 99%