Waste classification using AutoEncoder network with integrated feature selection method in convolutional neural network models

Abstract Background Drug sensitivity prediction and drug responsive biomarker selection on high-throughput genomic data is a critical step in drug discovery. Many computational methods have been developed to serve this purpose including several deep neural network models. However, the modular relations among genomic features have been largely ignored in these methods. To overcome this limitation, the role of the gene co-expression network on drug sensitivity prediction is investigated in this study. Methods In this paper, we first introduce a network-based method to identify representative features for drug response prediction by using the gene co-expression network. Then, two graph-based neural network models are proposed and both models integrate gene network information directly into neural network for outcome prediction. Next, we present a large-scale comparative study among the proposed network-based methods, canonical prediction algorithms (i.e., Elastic Net, Random Forest, Partial Least Squares Regression, and Support Vector Regression), and deep neural network models for drug sensitivity prediction. All the source code and processed datasets in this study are available at https://github.com/compbiolabucf/drug-sensitivity-prediction. Results In the comparison of different feature selection methods and prediction methods on a non-small cell lung cancer (NSCLC) cell line RNA-seq gene expression dataset with 50 different drug treatments, we found that (1) the network-based feature selection method improves the prediction performance compared to Pearson correlation coefficients; (2) Random Forest outperforms all the other canonical prediction algorithms and deep neural network models; (3) the proposed graph-based neural network models show better prediction performance compared to deep neural network model; (4) the prediction performance is drug dependent and it may relate to the drug’s mechanism of action. Conclusions Network-based feature selection method and prediction models improve the performance of the drug response prediction. The relations between the genomic features are more robust and stable compared to the correlation between each individual genomic feature and the drug response in high dimension and low sample size genomic datasets.

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Robustness of convolutional neural network models in hyperspectral noisy datasets with loss functions

Computers & Electrical Engineering ◽

10.1016/j.compeleceng.2021.107009 ◽

2021 ◽

Vol 90 ◽

pp. 107009

Author(s):

Sepehr Ghafari ◽

Milad Ghobadi Tarnik ◽

Hadi Sadoghi Yazdi

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Network Models ◽

Loss Functions ◽

Neural Network Models

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Epoch interrogation for skin cancer detection using convolutional neural network models

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/1074/1/012025 ◽

2021 ◽

Vol 1074 (1) ◽

pp. 012025

Author(s):

A Poornima ◽

M Shyamala Devi ◽

M Sumithra ◽

Mullaguri Venkata Bharath ◽

Swathi ◽

...

Keyword(s):

Neural Network ◽

Skin Cancer ◽

Convolutional Neural Network ◽

Cancer Detection ◽

Network Models ◽

Neural Network Models ◽

Skin Cancer Detection

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Systematic review of research design and reporting of imaging studies applying convolutional neural networks for radiological cancer diagnosis

European Radiology ◽

10.1007/s00330-021-07881-2 ◽

2021 ◽

Author(s):

Robert J. O’Shea ◽

Amy Rose Sharkey ◽

Gary J. R. Cook ◽

Vicky Goh

Keyword(s):

Neural Network ◽

Systematic Review ◽

Convolutional Neural Network ◽

Cancer Diagnosis ◽

Model Performance ◽

Network Models ◽

Imaging Studies ◽

Neural Network Models ◽

Eligibility Criteria ◽

Data Partitions

Abstract Objectives To perform a systematic review of design and reporting of imaging studies applying convolutional neural network models for radiological cancer diagnosis. Methods A comprehensive search of PUBMED, EMBASE, MEDLINE and SCOPUS was performed for published studies applying convolutional neural network models to radiological cancer diagnosis from January 1, 2016, to August 1, 2020. Two independent reviewers measured compliance with the Checklist for Artificial Intelligence in Medical Imaging (CLAIM). Compliance was defined as the proportion of applicable CLAIM items satisfied. Results One hundred eighty-six of 655 screened studies were included. Many studies did not meet the criteria for current design and reporting guidelines. Twenty-seven percent of studies documented eligibility criteria for their data (50/186, 95% CI 21–34%), 31% reported demographics for their study population (58/186, 95% CI 25–39%) and 49% of studies assessed model performance on test data partitions (91/186, 95% CI 42–57%). Median CLAIM compliance was 0.40 (IQR 0.33–0.49). Compliance correlated positively with publication year (ρ = 0.15, p = .04) and journal H-index (ρ = 0.27, p < .001). Clinical journals demonstrated higher mean compliance than technical journals (0.44 vs. 0.37, p < .001). Conclusions Our findings highlight opportunities for improved design and reporting of convolutional neural network research for radiological cancer diagnosis. Key Points • Imaging studies applying convolutional neural networks (CNNs) for cancer diagnosis frequently omit key clinical information including eligibility criteria and population demographics. • Fewer than half of imaging studies assessed model performance on explicitly unobserved test data partitions. • Design and reporting standards have improved in CNN research for radiological cancer diagnosis, though many opportunities remain for further progress.

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