使用概率神经网络 (PNN) 预测口腔癌
Mahendrakan Kantharimuthu1, Malathi M2, Sinthia P3
1Department of ECE, Hindusthan Institute of Technology, Coimbatore, India.
Asian Pacific journal of cancer prevention : APJCP
|September 29, 2023
概括
在印度,早期发现口腔癌对于改善患者存活率至关重要. 这项研究提出了一种具有离散波形变换的概率神经网络 (PNN),可以达到80%的准确性,用于准确的口腔恶性瘤预测.
科学领域:
- 在瘤学瘤学.
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 在印度,口腔癌通常是在晚期被诊断出来的,因此需要早期检测方法.
- 早期发现口腔癌可显著改善患者的预后和生存率.
- 由于病变异质性,早期检测口腔恶性瘤存在相当大的挑战.
研究的目的:
- 开发和评估用于早期检测口腔癌的计算机辅助诊断工具.
- 用先进的计算技术提高口腔恶性瘤预测的准确性.
- 为应对识别口腔癌在初期阶段的挑战.
主要方法:
- 利用概率神经网络 (PNN) 进行口腔恶性瘤预测.
- 集成的离散波段转换与PNN,以提高癌细胞识别的准确性.
- 探索各种计算机视觉技术来分析口腔病变.
主要成果:
- 该PNN模型在预测口腔恶性瘤方面实现了80%的分类准确度.
- 结合PNN和离散波纹变换,在准确预测癌细胞方面表现出有效性.
- 研究了计算机视觉技术,以克服识别异质口腔病变的挑战.
结论:
- 有效的口腔查对于及时对口腔病变做出决定至关重要.
- 基于准确查的迅速转诊可以显著降低口腔癌死亡率.
- 提出的基于PNN的方法对早期和准确的口腔癌检测有希望.
更多相关视频
03:05Author Spotlight: Advancing Early Detection and Treatment of Gastrointestinal Tumors
Published on: February 16, 2024
1.1K
04:09Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
Published on: October 10, 2018
8.3K
相关概念视频
01:21Cancer Survival Analysis
380
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
380
01:03Prediction Intervals
2.3K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
2.3K
01:13Sensitivity, Specificity, and Predicted Value
481
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...
481
01:14Probability in Statistics
13.4K
Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
13.4K
01:32Probability Distributions
7.2K
The probability of a random variable x is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
7.2K
01:17Probability Histograms
11.7K
A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
11.7K