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

TL;DR

This study applies LeNet, a deep learning CNN model, to classify lung cancer from CT images, achieving high accuracy and promising results for clinical application.

Contribution

It evaluates LeNet's effectiveness on a specific lung cancer dataset, demonstrating its potential for aiding diagnosis in clinical settings.

Findings

Accuracy of 99.51% in lung tumor classification

Sensitivity of 93% and specificity of 95%

Outperforms existing methods in the dataset

Abstract

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.