MRI-Based Brain Tumor Classification Using Dilated Parallel Deep Convolutional Neural Network with Ensemble of Machine Learning Classifiers

Published: May 7, 2024

Brain tumors are frequently classified with high accuracy using convolutional neural networks (CNNs) and better comprehend the spatial connections among pixels in complex pictures. Due to their tiny receptive fields, majority of deep network (DCNN)-based techniques overfit unable extract global context information from more significant regions. While dilated convolution retains data resolution at output layer increases field without adding computation, stacking several convolutions has drawback producing a grid effect. To handle gridding artifacts both coarse fine features images, this research suggests parallel (PDCNN) architecture that preserves wide field. reduce complexity, initially, input images resized then grayscale transformed. Data augmentation since been used expand number datasets. Dilated PDCNN makes use lower computational overhead contributes reduction artifacts. By contrasting various dilation rates, path uses low rate (2,1,1), while local (4,2,1) for decremental even numbers tackle two paths. Using three different types MRI datasets, suggested average ensemble method performs better. The provided by Multiclass Kaggle dataset-III, Figshare dataset-II, Binary tumor identification dataset-I is 98.35%, 98.13%, 98.67%, respectively. In comparison state-of-the-art techniques, structure improves results extracting features, making it efficient.

Language: Английский

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Deep learned features selection algorithm: Removal operation of anomaly feature maps (RO-AFM)

Applied Soft Computing, Journal Year: 2024, Volume and Issue: 162, P. 111809 - 111809

Published: May 26, 2024

Language: Английский

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HPClas: A data‐driven approach for identifying halophilic proteins based on catBoost

mLife, Journal Year: 2024, Volume and Issue: 3(4), P. 515 - 526

Published: July 20, 2024

Abstract Halophilic proteins possess unique structural properties and show high stability under extreme conditions. This distinct characteristic makes them invaluable for application in various aspects such as bioenergy, pharmaceuticals, environmental clean‐up, energy production. Generally, halophilic are discovered characterized through labor‐intensive time‐consuming wet lab experiments. In this study, we introduce the Protein Classifier (HPClas), a machine learning‐based classifier developed using catBoost ensemble learning technique to identify proteins. Extensive silico calculations were conducted on large public dataset of 12,574 samples HPClas achieved an area receiver operating curve (AUROC) 0.844 independent test set 200 samples. The source code curated publicly available at https://github.com/Showmake2/HPClas . conclusion, can be explored promising tool aid identification accelerate their different fields.

Language: Английский

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CNN-Based Hybrid Performance Evaluation Towards Online News Sentiment Classification Task

Published: Oct. 9, 2024

Language: Английский

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Optimal variational mode decomposition based automatic stress classification system using EEG signals

Applied Acoustics, Journal Year: 2024, Volume and Issue: 231, P. 110478 - 110478

Published: Dec. 18, 2024

Language: Английский

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