Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关实验视频

通过使用深度学习技术的整微方程来检测癌症的综合框架.

Tanneeru Gopisairam1, Srinivasarao Thota2, Thulasi Bikku3

  • 1Department of Mathematics, Amrita School of Physical Sciences, Amrita Vishwa Vidyapeetham, Amaravati, Andhra Pradesh, 522503, India.

Scientific reports
|February 18, 2026
PubMed
概括
此摘要是机器生成的。

相关概念视频

Adaptive Mechanisms in Cancer Cells02:53

Adaptive Mechanisms in Cancer Cells

7.2K
Cancer cells accumulate genetic changes at an abnormally rapid rate due to the defects in the DNA repair mechanisms. From an evolutionary perspective, such genetic instability is advantageous for cancer development. Mutant cell lines accumulate a series of beneficial mutations that contribute to their progression into cancer.
Some of the advantages that cancer cells have on normal cells include - enhanced ability to divide without terminally differentiating, induce new blood vessel formation,...
7.2K
Applications of Integration to Find Blood Flow01:27

Applications of Integration to Find Blood Flow

69
Blood flow through a cylindrical blood vessel can be mathematically described using the principles of laminar flow, a regime in which fluid moves smoothly in parallel layers. In this model, the velocity of the blood is not uniform across the cross-section of the vessel; rather, it varies with the radial distance from the center. The maximum velocity occurs along the central axis, decreasing progressively toward the vessel walls, where it reaches zero due to viscous drag.Approximating Blood...
69

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Optimized fetal head circumference estimation in 2D ultrasound using EfficientNet-B7 and Adam optimizer.

BMC pediatrics·2026
Same author

Deep Learning-Driven Early Diagnosis of Respiratory Diseases using CNN-RNN Fusion on Lung Sound Data.

Scientific reports·2025
Same author

Explainable deep reinforcement learning for climate forecasting with transfer learning.

Environmental science and pollution research international·2025
Same author

MSRP-TODNet: a multi-scale reinforced region wise analyser for tiny object detection.

BMC research notes·2025
Same author

Hybrid optimization technique for matrix chain multiplication using Strassen's algorithm.

F1000Research·2025
Same author

A novel optimization-driven deep learning framework for the detection of DDoS attacks.

Scientific reports·2024
Same journal

Serum vitamin D level and its association with vertigo frequency and severity in Meniere disease.

Scientific reports·2026
Same journal

PFA-Net: a physics-informed feature enhancement and attention network for interpretable bearing fault diagnosis under strong noise.

Scientific reports·2026
Same journal

Circulating inflammatory, redox, and apoptosis-related alterations in drug-naive idiopathic pulmonary fibrosis: an exploratory case-control study.

Scientific reports·2026
Same journal

A baseline-oriented dynamic aggregation approach for demand-side heterogeneous controllable resources.

Scientific reports·2026
Same journal

Temporal precision and accuracy in schizophrenia: an exploratory study.

Scientific reports·2026
Same journal

Prefrontal EEG spectral and nonlinear signatures of subthreshold depression during resting state and affectively valenced picture/video viewing: a participant-level analysis.

Scientific reports·2026
查看所有相关文章

这项研究引入了一种新的深度学习框架,用于在乳房影像中检测癌症. 通过将2D图像转换为1D信号,它可以在识别癌症区域方面实现高精度.

科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 计算生物学 计算生物学

背景情况:

  • 从医学图像中准确诊断癌症至关重要,但具有挑战性.
  • 深度学习为提高诊断准确性提供了有前途的计算工具.

研究的目的:

  • 开发和评估一个新的框架,用于检测癌症在乳房影像.
  • 用数学模型提高计算癌症诊断的解释性.

主要方法:

  • 将二维乳房影像转换为一维信号以提取特征.
  • 使用1D卷积神经网络进行图像分类.
  • 结合整微分方程来建模瘤动态和强度变化.

主要成果:

  • 在INbreast和MIAS数据集上的二进制分类中获得了96.4%的准确性.
  • 与传统的深度学习基准表现相似或优越的表现.
  • 在特征提取和计算效率方面的突出优势.

结论:

  • 拟议的框架显示出在乳房影像中进行准确和可解释的癌症诊断的巨大潜力.
  • 需要进一步的研究来解决诸如数据依赖性和信号转换期间信息丢失等局限性.
关键词:
癌症检测 癌症检测不同方程的微分方程.可解释的人工智能图像处理 图像处理分段化 分段化 分段化 分段化转移学习转移学习

相关实验视频