Name: Mastering Deep Learning for Cancer detection
Rating: 4.2 (23 reviews)

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Created byWilliam Farokhzad

Last updated 3/2024

English

What you'll learn

Learn python programming
Learn Image processing with python and openCV
Using object detection for detect special materials in pictures or videos
Make a new dataset for finding cancers
Cancer detection with deep learning

Course content

5 sections • 34 lectures • 6h 25m total length

Course structure2:35
Introduction Python2:10
Hello World4:41
Set up the coding environment with Visual Studio Code and Python, install the Python extension, create a Python file, and run a first hello world program to start practicing.
Variables 15:35
Explore how to create variables in Python, assign values with the assignment operator, and name containers clearly to store data.
Variables 28:22
Master variables and basic arithmetic operators in Python, performing addition, subtraction, multiplication, and division. Print results to view outputs in the terminal.
Data Type 15:37
Master the basics of Python data types, including integers, decimal numbers, and strings, and learn how to assign values to variables and concatenate text for simple programs.
Data Type 25:23
Data Type 37:32
Learn how to work with Python lists in Visual Studio, from creating lists with square brackets and zero-based indexing to accessing, modifying, appending, and removing items.
Data Type 410:59

Inputs8:43
Conditions9:59
Explore how to implement Python conditional statements using if, elif, and else to control program flow with logical operators and real-world examples like age checks and driving eligibility.
Repetitive execution9:59
Master for loops in Python to repeat code, iterate lists and ranges, apply conditionals, and practice nested loops in Visual Studio Code for automated tasks.
While Loops7:05
Functions8:51
Learn to define and use functions in Python to organize code and return results. Create a say hello function, call it with a name, and return sums.

Logical Operations on Images15:12
Thresholding17:08
Morphological Operation18:31
Edge Detection14:51
Explore edge detection techniques: Laplacian, Sobel (X and Y), and Canny, and implement them in Python using OpenCV to detect object boundaries in grayscale images.
Corner Detection23:33
Template matching15:31
Master template matching for object detection by locating a small template in a larger image using OpenCV and Python. Apply grayscale conversion, threshold, and bounding boxes to visualize detected matches.
Histogram18:40

Introduction Deep learning20:41
Introduction YOLO 14:54
Discover how YOLO achieves fast and efficient object detection by combining classification and localization, and compare it with R-CNN family methods to understand real-time limitations.
Introduction YOLO 24:39
Introduction YOLO 33:28
Installing libraries4:22
Cancer Detection34:25
Training Part 113:22
Training Part 216:59
Learn to prepare a custom cancer detection dataset using Roboflow, from signing up and creating a project to labeling, annotating, and exporting with YOLO version eight and augmentation.
Training Part 39:20
Export the dataset from Roboflow and train YOLO v8 on a custom dataset in Google Colab. Configure the yaml, select type X for accuracy, and download the best results.
Training Part 4 - Test Cancer Detection Program8:39
Test a cancer detection dataset with Python object detection, loading the dataset, labeling cancer in coco2.txt, resizing images to 640 by 640, and drawing bounding boxes with confidence scores.

Requirements

You can learn Python, Image processing and deep learning and everything you need to know.
No programming experience needed.

Description

Brief Description:

Unlock the power of deep learning to make a meaningful impact on global health. "Mastering Deep Learning for Cancer Detection" is not just a course; it's a call to action. Join us in the quest to find cancers across all ages and demographics, uniting individuals driven by a shared purpose: helping others and contributing to the solution for one of Earth's most significant challenges.

Key Objectives:

Inclusive Approach: This course is open to all individuals passionate about leveraging their skills to address the global issue of cancer. Whether you're a student, professional, or simply someone eager to make a difference, this course is for you.
Comprehensive Training: Gain expertise in utilizing deep learning methodologies for cancer detection. From understanding the fundamentals to implementing advanced techniques, this course covers the spectrum to ensure you're well-equipped to contribute meaningfully.
Community Impact: Be part of a community dedicated to applying technology for the greater good. Collaborate with like-minded individuals, share insights, and collectively contribute to the ongoing improvement of cancer detection strategies.
Global Perspective: Cancer knows no boundaries. This course empowers you to address this universal challenge, providing tools and knowledge applicable across diverse populations and demographics.

Who Should Enroll:

Individuals of all ages with a passion for helping others.
Students aspiring to make a difference in healthcare.
Professionals seeking to apply their skills to a global health cause.
Anyone committed to contributing to the solution for cancer detection.

Embark on this journey with us, where your expertise in deep learning becomes a catalyst for positive change. Enroll in "Mastering Deep Learning for Cancer Detection" and become part of a community dedicated to making a real impact on our planet's health. Together, we can turn the tide against cancer.

Who this course is for:

Students
Beginners
Professionals
Who want to help peoples

What you'll learn

Explore related topics

Course content

Python for Beginners9 lectures • 53min

Advanced Python5 lectures • 45min

Image Processing for Begineers3 lectures • 44min

Advanced Image Processing7 lectures • 2hr 3min

Cancer Detection10 lectures • 2hr 1min

Requirements

Description

Who this course is for: