Feature extraction and I-NB classification of CT images for early lung cancer detection

Karthiga, B.; Rekha, M.

doi:10.1016/j.matpr.2020.04.896

Cited by 10 publications

(2 citation statements)

References 5 publications

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“…Machine learning (ML) in drug discovery involves the use of statistical tools for learning and predicting molecular properties including bioactivity [1][2][3]. Popular ML algorithms in this field include Random Forests (RF); [2,5] Naïve Bayesian (NB) [5][6][7][8]; eXtreme Gradient Boosting (XGBoost) [2,9]; K-nearest neighbors (kNN) [2,10]; and probabilistic neural networks (PNN) [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Augmenting bioactivity by docking‐generated multiple ligand poses to enhance machine learning and pharmacophore modelling: discovery of new TTK inhibitors as case study

AL‐Imam,

Daoud,

Hatmal

et al. 2023

Molecular Informatics

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Dual specificity protein kinase threonine/Tyrosine kinase (TTK) is one of the mitotic kinases. High levels of TTK are detected in several types of cancer. Hence, TTK inhibition is considered a promising therapeutic anti‐cancer strategy. In this work, we used multiple docked poses of TTK inhibitors to augment training data for machine learning QSAR modeling. Ligand‐Receptor Contacts Fingerprints and docking scoring values were used as descriptor variables. Escalating docking‐scoring consensus levels were scanned against orthogonal machine learners, and the best learners (Random Forests and XGBoost) were coupled with genetic algorithm and Shapley additive explanations (SHAP) to determine critical descriptors for predicting anti‐TTK bioactivity and for pharmacophore generation. Three successful pharmacophores were deduced and subsequently used for in silico screening against the NCI database. A total of 14 hits were evaluated in vitro for their anti‐TTK bioactivities. One hit of novel chemotype showed reasonable dose‐response curve with experimental IC50 of 1.0 μM. The presented work indicates the validity of data augmentation using multiple docked poses for building successful machine learning models and pharmacophore hypotheses.

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Section: Introductionmentioning

confidence: 99%

Augmenting bioactivity by docking‐generated multiple ligand poses to enhance machine learning and pharmacophore modelling: discovery of new TTK inhibitors as case study

AL‐Imam,

Daoud,

Hatmal

et al. 2023

Molecular Informatics

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show abstract

“…However, the feature extraction technique was not used to perform the image denoising. [9] developed the Artificial Bee Colony Algorithm for the selection and classification of cancer features. The classification time, on the other hand, was not shortened.…”

Section: Introductionmentioning

confidence: 99%

Deep Convolution Neural Networks Learned Image Classification for Early Cancer Detection Using Lightweight

ooruchintala³

et al. 2021

Preprint

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Cancer is uncontrolled cell growth in any part of the body. Early cancer detection aims to identify patients who exhibit symptoms early on in order to maximise their chances of a successful treatment. Cancer disease mortality is decreased through early detection and treatment. Numerous researchers proposed a variety of image processing and machine learning approaches for cancer detection. However, existing systems did not improve detection accuracy or efficiency. A Deep Convolutional Neural Learning Classifier Model based on the Least Mean Square Filterative Ricker Wavelet Transform (L-DCNLC) is proposed to address the aforementioned issues. The L-DCNLC Model's primary objective is to detect cancer earlier by utilising a fully connected max pooling deep convolutional network with increased accuracy and reduced time consumption. The fully connected max pooling deep convolutional network is composed of one input layer, three hidden layers, and one output layer. Initially, the input layer of the L-DCNLC Model considers the number of patient images in the database as input.

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Lung cancer detection using novel residual unity AlexNet-based optimized mish dropout-deep convolutional neural network

Reddy¹,

Bharathi²

2023

Soft Comput

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Feature extraction and I-NB classification of CT images for early lung cancer detection

Cited by 10 publications

References 5 publications

Augmenting bioactivity by docking‐generated multiple ligand poses to enhance machine learning and pharmacophore modelling: discovery of new TTK inhibitors as case study

Augmenting bioactivity by docking‐generated multiple ligand poses to enhance machine learning and pharmacophore modelling: discovery of new TTK inhibitors as case study

Deep Convolution Neural Networks Learned Image Classification for Early Cancer Detection Using Lightweight

Lung cancer detection using novel residual unity AlexNet-based optimized mish dropout-deep convolutional neural network

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