What is it about?
This study aimed to apply pretrained EfficientNetB7 model to facilitate the process of classifying LC histopathology images as primary malignancy categories (adenocarcinoma, squamous cell carcinoma and large cell carcinoma) for early treatment of LC patients. Also, aims to analyse the performance of the proposed model using the accuracy measure. METHODS: The dataset of 15000 histopathology images of lung cancer were examined. EfficientNetB7, a special type of convolution neural network (CNN), pretrained with ImageNet for transfer learning were trained on this dataset. Accuracy metric was used for the evaluation of the proposed model.
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Why is it important?
Lung cancer (LC) is a harmful malignant tumor and potentially lethal illness. Therefore, early detection of LC is an urgent need, and dependent on the type of histology and the type of disease. The use of deep learning algorithms (DL) is required to analyse the histopathology images of LC and make treatment decisions accordingly. The employment of CNN based EfficientNetB7 model for the classification of LC based on histopathology images can speed up the diagnosis of LC and reduce the burden on pathologists for the early treatment of patients. The proposed model achieved 99.77% accuracy, while previous studies model achieved over 90 to 99% accuracy.
Perspectives
Writing this article was a great pleasure to our research team. I hope this article makes more insight into the lung cancer research using deep learning models. More than anything else, and if nothing else, I hope you find this article thought-provoking.
Anandhavalli Muniasamy
King Khalid University
Read the Original
This page is a summary of: Lung cancer histopathology image classification using transfer learning with convolution neural network model, Technology and Health Care, November 2023, IOS Press,
DOI: 10.3233/thc-231029.
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