Optimizing Lung Cancer Detection in CT Imaging: A Wavelet Multi-Layer Perceptron (WMLP) Approach Enhanced by Dragonfly Algorithm (DA)
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Abstract (arXiv)
Lung cancer stands as the preeminent cause of cancer-related mortality globally. Prompt and precise diagnosis, coupled with effective treatment, is imperative to reduce the fatality rates associated with this formidable disease. This study introduces a cutting-edge deep learning framework for the classification of lung cancer from CT scan imagery. The research encompasses a suite of image pre-processing strategies, notably Canny edge detection, and wavelet transformations, which precede the extraction of salient features and subsequent classification via a Multi-Layer Perceptron (MLP). The optimization process is further refined using the Dragonfly Algorithm (DA). The methodology put forth has attained an impressive training and testing accuracy of 99.82%, underscoring its efficacy and reliability in the accurate diagnosis of lung cancer.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 8 of its 61 references that are in the map. First posted on arXiv 27 Aug 2024.
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8 of 61 references.
References · 8
- 2018Highly accurate model for prediction of lung nodule malignancy with CT scanscited by 14
- 2018Automatic Lung Cancer Prediction from Chest X-ray Images Using Deep Learning Approachcited by 12
- 2023LCDctCNN: Lung Cancer Diagnosis of CT scan Images Using CNN Based Modelcited by 6
- 2024WIA-LD2ND: Wavelet-based Image Alignment for Self-supervised Low-Dose CT Denoisingcited by 4
- 2023Understanding MLP-Mixer as a Wide and Sparse MLPcited by 3
- 2021Segmentation of Lung Tumor from CT Images using Deep Supervisioncited by 2
- 2022Deep Learning Applications for Lung Cancer Diagnosis: A systematic reviewcited by 2
- 2023High-risk Factor Prediction in Lung Cancer Using Thin CT Scans: An Attention-Enhanced Graph Convolutional Network Approachcited by 2
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