# Evaluating LeNet Algorithms in Classification Lung Cancer from Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases

arXiv: https://arxiv.org/abs/2305.13333 · HTML: https://mlsanity.com/paper/2305.13333/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 47 references that are in the map.
- First posted on arXiv 19 May 2023.

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

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Modeling and forecasting Spread of COVID-19 epidemic in Iran until Sep 22, 2021, based on deep learning](https://mlsanity.com/paper/2103.08178/) (2021)
- [Deep Neural Network Based Ensemble learning Algorithms for the healthcare system (diagnosis of chronic diseases)](https://mlsanity.com/paper/2103.08182/) (2021)
- [New Descriptor for Glomerulus Detection in Kidney Microscopy Image](https://mlsanity.com/paper/1506.05920/) (2015)
- [Hybrid stacked ensemble combined with genetic algorithms for Prediction of Diabetes](https://mlsanity.com/paper/2103.08186/) (2021)

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

- [Using VGG16 Algorithms for classification of lung cancer in CT scans Image](https://mlsanity.com/paper/2305.18367/) (2023)
- [Review of deep learning in healthcare](https://mlsanity.com/paper/2310.00727/) (2023)

## Abstract (arXiv)

The advancement of computer-aided detection systems had a significant impact on clinical analysis and decision-making on human disease. Lung cancer requires more attention among the numerous diseases being examined because it affects both men and women, increasing the mortality rate. LeNet, a deep learning model, is used in this study to detect lung tumors. The studies were run on a publicly available dataset made up of CT image data (IQ-OTH/NCCD). Convolutional neural networks (CNNs) were employed in the experiment for feature extraction and classification. The proposed system was evaluated on Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases datasets the success percentage was calculated as 99.51%, sensitivity (93%) and specificity (95%), and better results were obtained compared to the existing methods. Development and validation of algorithms such as ours are important initial steps in the development of software suites that could be adopted in routine pathological practices and potentially help reduce the burden on pathologists.