Deep Learning Tutorial Part 3: Classifying Bacteria
Channel: Fuenfgeld
Date: 2021-05-29

In recent years, remarkable progress has been made in deep learning. The medical image analysis community has taken notice of these pivotal developments as the number of publications is increasing exponentially. 

In this video we will answer the following questions: How to use a convolutional neural network? How to use CNN? How to use CNN in python? How to train the network? How to adapt a model to a new image dataset? Transfer learning? How to validate the network? How to test the network? How to do medical image analysis? How to do medical image processing? How to do medical image classification? How to do better classification? How can we conduct deep learning visualization?

For this purpose, we first have to download the CNN models and folders containing the images for training, validation and testing. The image data is located in directories, one for training, one for validation and one for testing. Each directory has two sub-folders: positive and negative bacterial gram stains. A classifier is to be trained and is thus not downloaded. Normally, a default layer includes the classifier with many predefined classes. The model is trained (with certain configuration of the epoch argument) with the training data set. It is validated with the validation data set and tested with the test data set. The model can be adjusted by increasing the epoch number or re-training parts in original convolutional network (freezing/unfreezing layers).

Chapters
0:00 Introduction
0:34 Step 1: load packages and set datasets
2:35 Step 2: download model (without classifier) & define architecture
4:40 Step 3: configure layers to be trained
5:20 Step 4: Training
6:30 Step 5: Visualize training results: plotting learning curves
8:04 Step 6: Testing
8:30 Step 7: How to achieve a better model

Hashtags
#deeplearningtutorial
#deeplearningpython
#neuralnetwork
#convolutionalneuralnetwork