Abstract:
One of the most hazardous and severe diseases that people experience globally is colon
and lung cancer, which has spread to become a common medical issue. It is very
important to make a reliable and early discovery to reduce the danger of mortality. The
difficulty of the work ultimately depends on the histopathologists' experience. Underprepared
histologists may potentially endanger the patient's life. Recent times have seen a
rise in the popularity of deep learning, which is now appreciated in the interpretation of
medical imaging. In order to diagnose lung and colon cancer utilizing histopathological
image dataset and more effective augmentation approaches, this research aims to leverage
and transform the present pre-trained CNN-based architecture. In this study, the LC25000
dataset is used to train three different pre-trained Convolutional Neural Network models:
EffecientNetB7, ResNet50 and VGG16. The model performances are evaluated based on
accuracy, precision, recall & f1-score. The findings illustrate that all three models
produced impressive outcomes, ranging from 93% to 98% accuracy.
