Breast cancer is an unusual mass of the breast texture. It begins with an abnormal change in cell structure. This disease may increase uncontrollably and affects neighboring textures. Early diagnosis of this cancer (abnormal cell changes) can help definitively treat it. Also, prevention of this cancer can help to decrease the high cost of medical caring for breast cancer patients. In recent years, the computer-aided technique is an important active field for automatic cancer detection. In this study, an automatic breast tumor diagnosis system is introduced. An improved Deer Hunting Optimization Algorithm (DHOA) is used as the optimization algorithm. The presented method utilized a hybrid feature-based technique and a new optimized convolutional neural network (CNN). Simulations are applied to the DCE-MRI dataset based on some performance indexes. The novel contribution of this paper is to apply the preprocessing stage to simplifying the classification. Besides, we used a new metaheuristic algorithm. Also, the feature extraction by Haralick texture and local binary pattern (LBP) is recommended. Due to the obtained results, the accuracy of this method is 98.89%, which represents the high potential and efficiency of this method.

中文翻译：

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基于混合 CNN 和基于特征的方法使用改进的猎鹿优化算法在 MRI 中自动诊断乳腺肿瘤

乳腺癌是乳房质地异常的肿块。它始于细胞结构的异常变化。这种疾病可能会不受控制地增加并影响邻近的纹理。这种癌症的早期诊断（异常细胞变化）可以帮助明确治疗它。此外，预防这种癌症有助于降低乳腺癌患者的高昂医疗费用。近年来，计算机辅助技术是癌症自动检测的重要领域。本研究介绍了一种乳腺肿瘤自动诊断系统。改进的猎鹿优化算法（DHOA）被用作优化算法。所提出的方法利用基于混合特征的技术和新的优化卷积神经网络 (CNN)。基于一些性能指标将模拟应用于 DCE-MRI 数据集。本文的新贡献是应用预处理阶段来简化分类。此外，我们使用了一种新的元启发式算法。此外，推荐使用 Haralick 纹理和局部二值模式 (LBP) 进行特征提取。由于得到的结果，该方法的准确率为98.89%，代表了该方法的高潜力和高效率。