EfficientNet Algorithm for Classification of Different Types of Cancer
This work addresses the need for accurate and efficient cancer diagnosis in clinical practice, but it is incremental as it applies an existing algorithm to new medical data.
The paper tackled the problem of classifying various cancer types using the EfficientNet algorithm, achieving high accuracy, precision, recall, and F1 scores that outperformed other state-of-the-art methods on brain tumor, breast cancer, chest cancer, and skin cancer datasets.
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.