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.

翻译：准确高效地对不同类型癌症进行分类对于早期检测和有效治疗至关重要。本文展示了我们使用EfficientNet算法对脑肿瘤、乳腺癌钼靶、肺癌及皮肤癌进行分类的实验结果。我们使用公开数据集并对图像进行预处理以确保一致性和可比性。实验表明，EfficientNet算法在每个癌症数据集上均取得了高准确率、精确率、召回率和F1分数，优于文献中其他现有先进算法。我们还讨论了EfficientNet算法的优势与局限性及其在临床实践中的潜在应用。结果表明，EfficientNet算法非常适用于不同癌症类型的分类，可用于提升癌症诊断的准确性与效率。