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：翻译后的摘要：准确高效地区分不同类型癌症对于早期发现和有效治疗至关重要。本文介绍了我们使用EfficientNet算法对脑肿瘤、乳腺癌乳腺X线摄影、胸部癌和皮肤癌进行分类的实验结果。我们使用公开可得的数据集并对影像进行预处理以确保一致性和可比性。实验结果表明，EfficientNet算法在每个癌症数据集上均取得了高精度、高召回率和F1得分，优于文献中其他最先进的算法。我们还讨论了EfficientNet算法的优点和缺点，以及其在临床实践中的潜在应用。研究结果表明，EfficientNet算法非常适合于分辨不同类型的癌症，可以用于提高癌症诊断的准确性和效率。
相关内容
微信扫码咨询专知VIP会员