  Accurate and efficient classification of different types of cancer is
critical for early detection and effective treatment. In this paper, we present
the results of our experiments using the EfficientNet algorithm for
classification of brain tumor, breast cancer mammography, chest cancer, and
skin cancer. We used publicly available datasets and preprocessed the images to
ensure consistency and comparability. Our experiments show that the
EfficientNet algorithm achieved high accuracy, precision, recall, and F1 scores
on each of the cancer datasets, outperforming other state-of-the-art algorithms
in the literature. We also discuss the strengths and weaknesses of the
EfficientNet algorithm and its potential applications in clinical practice. Our
results suggest that the EfficientNet algorithm is well-suited for
classification of different types of cancer and can be used to improve the
accuracy and efficiency of cancer diagnosis.
