# LCDctCNN: Lung Cancer Diagnosis of CT scan Images Using CNN Based Model

arXiv: https://arxiv.org/abs/2304.04814 · HTML: https://mlsanity.com/paper/2304.04814/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 27 references that are in the map.
- First posted on arXiv 10 Apr 2023.

## Builds on (most cited first, 5 of 5)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [The Real-World-Weight Cross-Entropy Loss Function: Modeling the Costs of Mislabeling](https://mlsanity.com/paper/2001.00570/) (2020)
- [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)

## Built on by (most cited first, 6 of 6)

- [A Deep Analysis of Transfer Learning Based Breast Cancer Detection Using Histopathology Images](https://mlsanity.com/paper/2304.05022/) (2023)
- [Optimizing Lung Cancer Detection in CT Imaging: A Wavelet Multi-Layer Perceptron (WMLP) Approach Enhanced by Dragonfly Algorithm (DA)](https://mlsanity.com/paper/2408.15355/) (2024)
- [Transfer or Self-Supervised? Bridging the Performance Gap in Medical Imaging](https://mlsanity.com/paper/2407.05592/) (2024)
- [Classification based deep learning models for lung cancer and disease using medical images](https://mlsanity.com/paper/2507.01279/) (2025)
- [Enhanced Liver Tumor Detection in CT Images Using 3D U-Net and Bat Algorithm for Hyperparameter Optimization](https://mlsanity.com/paper/2508.08452/) (2025)
- [Geometric Calibration and Neutral Zones for Uncertainty-Aware Multi-Class Classification](https://mlsanity.com/paper/2511.20960/) (2025)

## Abstract (arXiv)

The most deadly and life-threatening disease in the world is lung cancer. Though early diagnosis and accurate treatment are necessary for lowering the lung cancer mortality rate. A computerized tomography (CT) scan-based image is one of the most effective imaging techniques for lung cancer detection using deep learning models. In this article, we proposed a deep learning model-based Convolutional Neural Network (CNN) framework for the early detection of lung cancer using CT scan images. We also have analyzed other models for instance Inception V3, Xception, and ResNet-50 models to compare with our proposed model. We compared our models with each other considering the metrics of accuracy, Area Under Curve (AUC), recall, and loss. After evaluating the model's performance, we observed that CNN outperformed other models and has been shown to be promising compared to traditional methods. It achieved an accuracy of 92%, AUC of 98.21%, recall of 91.72%, and loss of 0.328.