Detection and Classification of Lung Cancer in Histopathology Images Using Deep Learning

In recent years, artificial intelligence has been used to diagnose and classify cancers using different deep learning-based models. Although they have good overall accuracy, they need high computation resources and execution time, and also have low accuracy, precision, and sensitivity. To this end, we try to employ a new model named the "EfficientNetB0" model with appropriate preprocessing to obtain high precision and sensitivity at a relatively low computation time for diagnosing lung cancer. The EfficientNetB0 model consists of 7 blocks, and each block includes one layer of mobile convolution (MB-Conv) and squeeze-excitation (SE) blocks. EfficientNetB0 has a higher accuracy compared to other common deep learning models due to incorporating a compound coefficient approach. The proposed model is evaluated on the histopathology images dataset and the obtained accuracy of the model is 0.9258. Also, its precision and sensitivity are 0.942 and 0.967, respectively, and these show the superiority of this model compared to existing methods.