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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-science-cnn-opencv-chest-xray-pneumonia-detection/
课程评论：没有评论
Coursera 课程《数据科学：CNN & OpenCV：胸部X光肺炎检测》中文内容摘要 本课程面向希望学习如何利用人工智能和机器学习算法，通过胸部X光片检测肺炎的人们。课程将深入讲解如何构建并评估一个深度学习模型，以高精度预测X光片是否显示肺炎。课程内容涵盖 TensorFlow、CNN 和 OpenCV 的实际应用，引导学员完成从数据探索、数据增强、数据生成器、定制预训练模型（如 MobileNetV2）到模型构建、评估及最终部署的全过程。 课程主要内容分解： * **基础与准备**: 项目概览、Google Colab 介绍、项目和数据集文件夹结构理解、项目设置、配置文件和数据集创建文件的讲解。 * **数据探索与预处理**: 导入常用库、数据可视化（类别计数、样本展示）、文件数量统计、训练/验证准确率和损失函数可视化、计算类别权重、数据增强技术介绍与实现、数据生成器介绍与实现。 * **深度学习模型构建**: 卷积神经网络 (CNN) 和 OpenCV 基础、预训练模型（MobileNetV2）介绍、加载 MobileNetV2 分类器、构建全连接层和最终 MobileNetV2 模型、深入理解 CNN 关键组件（Conv2D, Filters, Relu, Batch Normalization, MaxPooling2D, Dropout, Flatten, Dense）、构建自定义 CNN 网络架构。 * **模型训练与评估**: 优化器（特别是 Adam Optimizer）和二元交叉熵损失函数的作用、整合 MobileNetV2 和自定义 CNN 模型进行训练、模型检查点 (Model Checkpoint) 的实现与应用、Epoch 和 Batch Size 的概念、使用 MobileNetV2 和自定义 CNN 模型进行训练（Fitting）。 * **结果分析与模型部署**: 使用训练好的模型对测试数据进行预测、分类报告 (Classification Report) 的介绍与应用、计算混淆矩阵并导出准确率、灵敏度和特异度、绘制训练/验证准确率和损失图。 * **模型保存与加载**: 模型序列化（保存到磁盘）、从云端加载模型、加载图像并使用模型进行肺炎预测。 课程特色： * **实操性强**: 这是一个实际操作的项目，提供端到端的实战指导。 * **全面深入**: 涵盖了从数据处理到模型部署的全流程。 * **技术栈**: 主要使用 TensorFlow、CNN 和 OpenCV。 * **云端环境**: 课程将在 Google Colab 中进行，方便学员学习。 * **认证与资源**: 完成课程可获得 AutomationGig 的结业证书，并提供 Jupyter Notebook 及项目文件。 该课程尤其适合在当前应对 COVID-19，需要快速准确诊断肺炎的情况下，学习利用先进的深度学习技术进行医疗诊断的人员。通过此课程，参与者将在短时间内显著提升在深度学习领域的掌握程度。 **免责声明**: 本课程及其相关内容仅用于教育目的。
If you want to learn the process to detect whether a person is having Pneumonia using Chest XRays with the help of AI and Machine Learning algorithms then this course is for you.In this course I will cover, how to build a model to predict whether an X-ray scan shows presence of pneumonia with very high accuracy using Deep Learning Models. This is a hands on project where I will teach you the step by step process in creating and evaluating a deep learning model using Tensorflow, CNN and OpenCV.This course will walk you through the initial data exploration and understanding, Data Augumentation,Data Generators,customizing pretrained Models like MobileNetV2, Model Checkpoints, model building and evaluation.Then using the trained model to detect the presence of Pneumonia using Chest XRays.I have splitted and segregated the entire course in Tasks below, for ease of understanding of what will be covered.Task 1 : Project Overview.Task 2 : Introduction to Google Colab.Task 3 : Understanding the project folder structure.Task 4 : Understanding the dataset and the folder structure.Task 5 : Setting up the project in Google Colab_Part1Task 6 : Setting up the project in Google Colab_Part2Task 7 : About Config and Create_Dataset FileTask 8 : Importing the Libraries.Task 9 : Plotting the count of data against each class in each directoryTask 10 : Plotting some samples from both the classesTask 11 : Creating a common method to get the number of files from a directoryTask 12 : Defining a method to plot training and validation accuracy and lossTask 13 : Calculating the class weights in train directoryTask 14: About Data Augmentation.Task 15: Implementing Data Augmentation techniques.Task 16: About Data Generators.Task 17: Implementing Data Generators.Task 18: About Convolutional Neural Network (CNN).Task 19: About OpenCV.Task 20: Understanding pre-trained models.Task 21: About MobileNetV2 model.Task 22: Loading the MobileNetV2 classifier.Task 23: Building a new fully-connected (FC) head.Task 24: Building the final MobileNetV2 model.Task 25: Understanding Conv2D, Filters, Relu activation, Batch Normalization, MaxPooling2D, Dropout, Flatten, DenseTask 26: Building a custom CNN network architecture.Task 27: Role of Optimizer in Deep Learning.Task 28: About Adam Optimizer.Task 29: About binary cross entropy loss function.Task 30: Putting all together for MobileNetV2.Task 31: Putting all together for Custom CNN Model.Task 32: About Model CheckpointTask 33: Implementing Model CheckpointTask 34: About Epoch and Batch Size.Task 35: MobileNetV2 and Custom CNN Model Fitting.Task 36: Predicting on the test data using both MobileNetV2 and Custom CNN ModelTask 37: About Classification Report.Task 38: Classification Report in action for both MobileNetV2 and Custom CNN Model.Task 39: Computing the confusion matrix and and using the same to derive the accuracy, sensitivity and specificity.Task 40: Plot training and validation accuracy and lossTask 41: Serialize/Writing the mode to diskTask 42: Loading the final model from driveTask 43: Loading an image and predicting using the model whether the person has Pneumonia.Machine learning has a phenomenal range of applications, including in health and diagnostics. This course will explain the complete pipeline from loading data to predicting results on cloud, and it will explain how to build an X-ray image classification model from scratch to predict whether an X-ray scan shows presence of pneumonia. This is especially useful during these current times as COVID-19 is known to cause pneumonia.Take the course now, and have a much stronger grasp of Deep learning in just a few hours!You will receive:1. Certificate of completion from AutomationGig.2. The Jupyter notebook and other project files are provided at the end of the course in the resource section.So what are you waiting for?Grab a cup of coffee, click on the ENROLL NOW Button and start learning the most demanded skill of the 21st century. We'll see you inside the course!Happy Learning!![Please note that this course and its related contents are for educational purpose only][Music: bensound]
