# Optimizing Lung Cancer Detection in CT Imaging: A Wavelet Multi-Layer Perceptron (WMLP) Approach Enhanced by Dragonfly Algorithm (DA)

arXiv: https://arxiv.org/abs/2408.15355 · HTML: https://mlsanity.com/paper/2408.15355/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 61 references that are in the map.
- First posted on arXiv 27 Aug 2024.

## Builds on (most cited first, 8 of 8)

- [Highly accurate model for prediction of lung nodule malignancy with CT scans](https://mlsanity.com/paper/1802.01756/) (2018)
- [Automatic Lung Cancer Prediction from Chest X-ray Images Using Deep Learning Approach](https://mlsanity.com/paper/1808.10858/) (2018)
- [WIA-LD2ND: Wavelet-based Image Alignment for Self-supervised Low-Dose CT Denoising](https://mlsanity.com/paper/2403.11672/) (2024)
- [LCDctCNN: Lung Cancer Diagnosis of CT scan Images Using CNN Based Model](https://mlsanity.com/paper/2304.04814/) (2023)
- [Understanding MLP-Mixer as a Wide and Sparse MLP](https://mlsanity.com/paper/2306.01470/) (2023)
- [Segmentation of Lung Tumor from CT Images using Deep Supervision](https://mlsanity.com/paper/2111.09262/) (2021)
- [Deep Learning Applications for Lung Cancer Diagnosis: A systematic review](https://mlsanity.com/paper/2201.00227/) (2022)
- [High-risk Factor Prediction in Lung Cancer Using Thin CT Scans: An Attention-Enhanced Graph Convolutional Network Approach](https://mlsanity.com/paper/2308.14000/) (2023)

## Built on by (most cited first, 1 of 1)

- [Enhanced Liver Tumor Detection in CT Images Using 3D U-Net and Bat Algorithm for Hyperparameter Optimization](https://mlsanity.com/paper/2508.08452/) (2025)

## 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.