优化了使用贝叶斯优化用于检测耳部疾病的微调整合集分类器
Israa Elmorsy1, Waleed Moneir2, Ahmed I Saleh3
1Electronics and Communications Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt.
Computers in biology and medicine
|April 11, 2025
概括
一种新的深度学习模型通过耳镜图像准确诊断耳部疾病,提高了准确性并减少了外部和中耳疾病的误诊率. 这种自动化系统有助于及时治疗,防止听力损失.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 耳鼻喉科 耳鼻喉科 耳鼻喉科
背景情况:
- 外耳和中耳疾病很常见,特别是在儿童中.
- 延迟诊断和治疗耳部疾病可能导致听力损失.
- 当前的诊断方法依赖于耳鼻喉科医生的专业知识,这可能是主观的,容易出错.
研究的目的:
- 开发基于深度学习的自动化系统,用于诊断耳部疾病.
- 提高耳部疾病分类的准确性和特异性.
- 创建一个工具,以协助早期检测耳朵病理.
主要方法:
- 使用MobileNet和DenseNet169.9开发了一个加权平均投票组合分类器.
- 贝叶斯优化用于超参数调整.
- 该模型在282张口腔镜图像的公开数据集上进行了微调,不包括 Tympanostomy Tubes 类.
主要成果:
- 拟议的模型实现了99.54%的准确性,曲线下面积 (AUC) 为1.
- 使用Grad-CAM++突出度图来可视化光镜图像中的重要特征.
- 整体方法增强了用于耳部疾病检测的分类能力.
结论:
- 开发的深度学习模型显示了自动耳病分类的重大前景.
- 该系统可以提高诊断准确度,减少误诊率.
- 这种自动化工具有可能帮助临床医生更有效地诊断耳部疾病.
相关概念视频
Classification of Signals
342
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
342
Sensitivity, Specificity, and Predicted Value
145
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
Sensitivity is the...
145
Force Classification
1.0K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.0K
Classification of Systems-II
125
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
125
Classification of Systems-I
161
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
161
Expected Frequencies in Goodness-of-Fit Tests
2.5K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).
2.5K


