phat 24(1):

Research Article

Real Time Lung Cancer Classification with YOLOv5

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  • @ARTICLE{10.4108/eetpht.9.3925,
        author={Shaif Mehraj Makhdoomi and Cherry Khosla and Sagar Dhanraj Pande},
        title={Real Time Lung Cancer Classification with YOLOv5},
        journal={EAI Endorsed Transactions on Pervasive Health and Technology},
        keywords={Adenocarcinoma, Artificial Intelligence, Automated diagnosis, Classification, Deep Learning, Image Analysis, Large Cell Carcinoma, Lung Cancer Classification, Machine Learning, Medical Imaging},
  • Shaif Mehraj Makhdoomi
    Cherry Khosla
    Sagar Dhanraj Pande
    Year: 2023
    Real Time Lung Cancer Classification with YOLOv5
    DOI: 10.4108/eetpht.9.3925
Shaif Mehraj Makhdoomi1, Cherry Khosla1, Sagar Dhanraj Pande2,*
  • 1: Lovely Professional University
  • 2: Vellore Institute of Technology University
*Contact email:


Cancer must be appropriately categorized for effective diagnosis and treatment. Deep learning algorithms have shown tremendous promise in recent years for automating cancer classification. We used the deep learning system YOLOv5 to classify the four types of lung cancer in this study: big cell carcinoma, adenocarcinoma, normal lung tissue, and squamous cell carcinoma. We trained the YOLOv5 model using a publicly available database of lung cancer pictures. The dataset was divided into four categories: big cell carcinoma, adenocarcinoma, normal lung tissue, and squamous cell cancer. In addition, we compared YOLOv5's performance to older models such as SVM, RF, ANN, and CNN. The comparison found that YOLOv5 outperformed all these models, indicating its potential for the development of more accurate and efficient autonomous cancer classification systems. Conclusions from the research have important implications for cancer identification and therapy. Automatic cancer classification systems have the potential to increase the accuracy and efficacy of cancer detection, perhaps leading to better patient outcomes. The accuracy and speed of these systems can be enhanced by using deep learning techniques like YOLOv5, making them more effective for clinical applications. Our study's findings demonstrated high accuracy for every class, with a total accuracy of 97.77%. With the aid of accuracy, train loss, and test loss graphs, we assessed the model's performance. The graphs demonstrated how the model was able to gain knowledge from the data and increase its accuracy as it was being trained. The study's findings were also compiled in a table that gave a thorough assessment of each class's accuracy.