EfficientNet Algorithm for Classification of Different Types of Cancer

TL;DR

This paper demonstrates that the EfficientNet algorithm effectively classifies various cancer types with high accuracy, outperforming existing methods, and shows promise for clinical cancer diagnosis applications.

Contribution

The study applies EfficientNet to multiple cancer datasets, showing its superior performance and potential for improving diagnostic accuracy in clinical settings.

Findings

EfficientNet achieved high accuracy across all cancer datasets.

Outperformed other state-of-the-art algorithms in classification tasks.

Indicated potential for clinical application in cancer diagnosis.

Abstract

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.