Biomedical Image Analysis for Colon and Lung Cancer Detection Using Tuna Swarm Algorithm With Deep Learning Model

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 94705 - 94712

Published: Jan. 1, 2023

The domain of Artificial Intelligence (AI) is made important strides recently, leading to developments in several domains comprising biomedical diagnostics and research. procedure AI-based systems analytics takes opened up novel avenues for the progress disease analysis, drug discovery, treatment. Cancer second major reason death worldwide; around one every six people pass away suffering from it. Among kinds cancers, colon lung variations are most frequent deadliest ones. Initial detection conditions on both fronts significantly reduces probability mortality. Deep learning (DL) Machine (ML) exploited speed such cancer detection, permitting researchers analyze a huge count patients lesser time at minimal cost. This study develops new Biomedical Image Analysis Colon Lung Detection using Tuna Swarm Algorithm with Learning (BICLCD-TSADL) model. presented BICLCD-TSADL technique examines images identification classification cancer. To accomplish this, applies Gabor filtering (GF) preprocess input images. In addition, employs GhostNet feature extractor create collection vectors. Moreover, AFAO was executed adjust hyperparameters technique. Furthermore, TSA echo state network (ESN) classifier utilized detecting demonstrate more incredible outcome system, an extensive experimental carried out. comprehensive comparative analysis highlighted greater efficiency other approaches maximum accuracy 99.33%.

Language: Английский

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Secure and Transparent Lung and Colon Cancer Classification Using Blockchain and Microsoft Azure

Advances in respiratory medicine, Journal Year: 2024, Volume and Issue: 92(5), P. 395 - 420

Published: Oct. 17, 2024

The global healthcare system faces challenges in diagnosing and managing lung colon cancers, which are significant health burdens. Traditional diagnostic methods inefficient prone to errors, while data privacy security concerns persist.

Language: Английский

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Lung and Colon Cancer Classification Using Multiscale Deep Features Integration of Compact Convolutional Neural Networks and Feature Selection

Technologies, Journal Year: 2025, Volume and Issue: 13(2), P. 54 - 54

Published: Feb. 1, 2025

The automated and precise classification of lung colon cancer from histopathological photos continues to pose a significant challenge in medical diagnosis, as current computer-aided diagnosis (CAD) systems are frequently constrained by their dependence on singular deep learning architectures, elevated computational complexity, ineffectiveness utilising multiscale features. To this end, the present research introduces CAD system that integrates several lightweight convolutional neural networks (CNNs) with dual-layer feature extraction selection overcome aforementioned constraints. Initially, it extracts attributes two separate layers (pooling fully connected) three pre-trained CNNs (MobileNet, ResNet-18, EfficientNetB0). Second, uses benefits canonical correlation analysis for dimensionality reduction pooling layer reduce complexity. In addition, features encapsulate both high- low-level representations. Finally, benefit multiple network architectures while reducing proposed merges dual variables then applies variance (ANOVA) Chi-Squared most discriminative integrated CNN architectures. is assessed LC25000 dataset leveraging eight distinct classifiers, encompassing various Support Vector Machine (SVM) variants, Decision Trees, Linear Discriminant Analysis, k-nearest neighbours. experimental results exhibited outstanding performance, attaining 99.8% accuracy cubic SVM classifiers employing merely 50 ANOVA-selected features, exceeding performance individual markedly diminishing framework’s capacity sustain exceptional limited set renders especially advantageous clinical applications where diagnostic precision efficiency critical. These findings confirm efficacy multi-CNN, multi-layer methodology enhancing mitigating constraints systems.

Language: Английский

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A quasi-oppositional learning of updating quantum state and Q-learning based on the dung beetle algorithm for global optimization

Alexandria Engineering Journal, Journal Year: 2023, Volume and Issue: 81, P. 469 - 488

Published: Sept. 22, 2023

There are many tricky optimization problems in real life, and metaheuristic algorithms the most effective way to solve at a lower cost. The dung beetle algorithm (DBO) is more innovative proposed 2022, which affected by action of beetles such as ball rolling, foraging, reproduction. Therefore, A based on quasi-oppositional learning Q-learning (QOLDBO). First, quantum state update idea cleverly integrated into increase randomness generated population. And best behavior pattern selected adding rolling stage improve search effect. In addition, variable spiral local domain method make up for shortage developing only around neighborhood optimum. For optimal solution each iteration, dimensional adaptive Gaussian variation retained. Experimental performance tests show that QOLDBO performs well both benchmark test functions CEC 2017. Simultaneously, validity verified several classical practical application engineering problems.

Language: Английский

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DRPSO:A multi-strategy fusion particle swarm optimization algorithm with a replacement mechanisms for colon cancer pathology image segmentation

Computers in Biology and Medicine, Journal Year: 2024, Volume and Issue: 178, P. 108780 - 108780

Published: June 22, 2024

Language: Английский

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A comprehensive survey of feature selection techniques based on whale optimization algorithm

Multimedia Tools and Applications, Journal Year: 2023, Volume and Issue: 83(16), P. 47775 - 47846

Published: Oct. 28, 2023

Language: Английский

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A Novel Heteromorphous Convolutional Neural Network for Automated Assessment of Tumors in Colon and Lung Histopathology Images

Biomimetics, Journal Year: 2023, Volume and Issue: 8(4), P. 370 - 370

Published: Aug. 16, 2023

The automated assessment of tumors in medical image analysis encounters challenges due to the resemblance colon and lung non-mitotic nuclei their heteromorphic characteristics. An accurate tumor presence is crucial for determining aggressiveness grading. This paper proposes a new method called ColonNet, heteromorphous convolutional neural network (CNN) with feature grafting methodology categorically configured analyzing mitotic histopathology images. ColonNet model consists two stages: first, identifying potential patches within histopathological imaging areas, second, categorizing these into squamous cell carcinomas, adenocarcinomas (lung), benign (colon), (colon) based on model’s guidelines. We develop employ our deep CNNs, each capturing distinct structural, textural, morphological properties nuclei, construct CNN. execution proposed analyzed by its comparison state-of-the-art CNNs. results demonstrate that surpasses others test set, achieving an impressive F1 score 0.96, sensitivity specificity 0.95, area under accuracy curve 0.95. These outcomes underscore hybrid superior performance, excellent generalization, accuracy, highlighting as valuable tool support pathologists diagnostic activities.

Language: Английский

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Multi head attention based conditional progressive GAN for colon cancer histopathological images analysis

Multimedia Tools and Applications, Journal Year: 2025, Volume and Issue: unknown

Published: March 21, 2025

Language: Английский

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Enhanced Crested Ibis Algorithm: Performance Validation in Benchmark Functions, Engineering Problem, and Application in Brain Tumor Detection

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 128231 - 128231

Published: May 1, 2025

Language: Английский

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ELW‐CNN: An extremely lightweight convolutional neural network for enhancing interoperability in colon and lung cancer identification using explainable AI

Healthcare Technology Letters, Journal Year: 2025, Volume and Issue: 12(1)

Published: Jan. 1, 2025

Abstract Cancer is a condition in which cells the body grow uncontrollably, often forming tumours and potentially spreading to various areas of body. hazardous medical case history analysis. Every year, many people die cancer at an early stage. Therefore, it necessary accurately identify effectively treat save human lives. However, machine deep learning models are effective for identification. effectiveness these efforts limited by small dataset size, poor data quality, interclass changes between lung squamous cell carcinoma adenocarcinoma, difficulties with mobile device deployment, lack image individual‐level accuracy tests. To overcome difficulties, this study proposed extremely lightweight model using convolutional neural network that achieved 98.16% large colon individually 99.02% 99.40% cancer. The used only 70 thousand parameters, highly real‐time solutions. Explainability methods such as Grad‐CAM symmetric explanation highlight specific regions input affect decision model, helping potential challenges. will aid professionals developing automated accurate approach detecting types

Language: Английский

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