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PSO-MCAE-LCCD: Particle Swarm Optimized MobileNetV2 Convolutional Autoencoder for Lung and Colon Cancer Detection

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Titel: PSO-MCAE-LCCD: Particle Swarm Optimized MobileNetV2 Convolutional Autoencoder for Lung and Colon Cancer Detection
Autoren: L.K. Suresh Kumar, K. Srinivasa Chakravarthy, M. Sathya Devi, P. Rathna Sekhar
Quelle: International Journal of Environmental Sciences. :714-722
Verlagsinformationen: Academic Science Publications and Distributions, 2025.
Publikationsjahr: 2025
Beschreibung: Identifying lung and colon cancer at an early stage significantly increases their chances of survival. This research proposes a new deep learning model PSO-MCAE-LCCD, which combines feature extraction using MobileNetV2 with classification using a Convolutional Autoencoder (CAE) implemented with Particle Swarm Optimization (PSO). To improve the median filtering, histopathological images from the LC25000 dataset were pre-processed. PSO optimally tunes the learning rate, dropout rate, and unit dense ratio, which significantly enhances model performance. After testing with 80:20 and 70:30 train-test splits, the model achieved high accuracy, precision, recall, and F1-score for all five cancer classes. Proposed model validation results showed 99.38% accuracy, outperforming other models in computation with a prediction time of 17.95 seconds. ROC and precision-recall curves validate model performance for all tested classes. Results show that PSO-MCAE-LCCD is a robust and efficient tool for automated histopathological cancer detection.
Publikationsart: Article
ISSN: 2229-7359
DOI: 10.64252/bdh6c567
Rights: CC BY
Dokumentencode: edsair.doi...........9ff09e22bde6236c29a53c242f5577e4
Datenbank: OpenAIRE
Beschreibung
Abstract:Identifying lung and colon cancer at an early stage significantly increases their chances of survival. This research proposes a new deep learning model PSO-MCAE-LCCD, which combines feature extraction using MobileNetV2 with classification using a Convolutional Autoencoder (CAE) implemented with Particle Swarm Optimization (PSO). To improve the median filtering, histopathological images from the LC25000 dataset were pre-processed. PSO optimally tunes the learning rate, dropout rate, and unit dense ratio, which significantly enhances model performance. After testing with 80:20 and 70:30 train-test splits, the model achieved high accuracy, precision, recall, and F1-score for all five cancer classes. Proposed model validation results showed 99.38% accuracy, outperforming other models in computation with a prediction time of 17.95 seconds. ROC and precision-recall curves validate model performance for all tested classes. Results show that PSO-MCAE-LCCD is a robust and efficient tool for automated histopathological cancer detection.
ISSN:22297359
DOI:10.64252/bdh6c567