# Transfer Learning by Cascaded Network to identify and classify lung nodules for cancer detection

arXiv: https://arxiv.org/abs/2009.11587 · HTML: https://mlsanity.com/paper/2009.11587/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2020.

## Built on by (most cited first, 2 of 2)

- [A Hybrid Deep Learning Framework with Explainable AI for Lung Cancer Classification with DenseNet169 and SVM](https://mlsanity.com/paper/2512.03359/) (2025)
- [A Cascaded Edge-Cloud Architecture for Automated Diabetic Retinopathy Screening](https://mlsanity.com/paper/2605.14108/) (2026)

## Abstract (arXiv)

Lung cancer is one of the most deadly diseases in the world. Detecting such tumors at an early stage can be a tedious task. Existing deep learning architecture for lung nodule identification used complex architecture with large number of parameters. This study developed a cascaded architecture which can accurately segment and classify the benign or malignant lung nodules on computed tomography (CT) images. The main contribution of this study is to introduce a segmentation network where the first stage trained on a public data set can help to recognize the images which included a nodule from any data set by means of transfer learning. And the segmentation of a nodule improves the second stage to classify the nodules into benign and malignant. The proposed architecture outperformed the conventional methods with an area under curve value of 95.67%. The experimental results showed that the classification accuracy of 97.96% of our proposed architecture outperformed other simple and complex architectures in classifying lung nodules for lung cancer detection.