Khan Rashid, Su Liyilei, Zaman Asim, Hassan Haseeb, Kang Yan, Huang Bingding
College of Applied Sciences, Shenzhen University, Shenzhen, 518060, China.
Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen, 518060, China.
Heliyon. 2024 May 6;10(10):e30528. doi: 10.1016/j.heliyon.2024.e30528. eCollection 2024 May 30.
Diagnosing liver disease presents a significant medical challenge in impoverished countries, with over 30 billion individuals succumbing to it each year. Existing models for detecting liver abnormalities suffer from lower accuracy and higher constraint metrics. As a result, there is a pressing need for improved, efficient, and effective liver disease detection methods. To address the limitations of current models, this method introduces a deep liver segmentation and classification system based on a Customized Mask-Region Convolutional Neural Network (cm-RCNN). The process begins with preprocessing the input liver image, which includes Adaptive Histogram Equalization (AHE). AHE helps dehaze the input image, remove color distortion, and apply linear transformations to obtain the preprocessed image. Next, a precise region of interest is segmented from the preprocessed image using a novel deep strategy called cm-RCNN. To enhance segmentation accuracy, the architecture incorporates the ReLU activation function and the modified sigmoid activation function. Subsequently, a variety of features are extracted from the segmented image, including ResNet features, shape features (area, perimeter, approximation, and convex hull), and enhanced median binary pattern. These extracted features are then used to train a hybrid classification model, which incorporates classifiers like SqueezeNet and DeepMaxout models. The final classification outcome is determined by averaging the scores obtained from both classifiers.
在贫困国家，诊断肝脏疾病是一项重大的医学挑战，每年有超过300亿人死于该疾病。现有的肝脏异常检测模型存在准确率较低和约束指标较高的问题。因此，迫切需要改进的、高效且有效的肝脏疾病检测方法。为了解决当前模型的局限性，该方法引入了一种基于定制掩码区域卷积神经网络（cm-RCNN）的深度肝脏分割和分类系统。该过程首先对输入的肝脏图像进行预处理，包括自适应直方图均衡化（AHE）。AHE有助于对输入图像去雾、消除颜色失真并应用线性变换以获得预处理图像。接下来，使用一种名为cm-RCNN的新型深度策略从预处理图像中分割出精确的感兴趣区域。为了提高分割精度，该架构采用了ReLU激活函数和改进的sigmoid激活函数。随后，从分割图像中提取各种特征，包括ResNet特征、形状特征（面积、周长、近似值和凸包）以及增强的中值二进制模式。然后，这些提取的特征用于训练一个混合分类模型，该模型包含SqueezeNet和DeepMaxout模型等分类器。最终的分类结果通过对两个分类器获得的分数进行平均来确定。
