Preparing the Acute Lymphoblastic Leukemia dataset: In this article I will cover the steps required to create the dataset required to train the model using the network we defined in the previous tutorial. The article will cover the paper exactly, and will use the original 108 image dataset (ALL_IDB1).

As part of my R&D for the AML/ALL AI Research Project, I am reviewing a selection of papers related to using Convolutional Neural Networks (CNN) for detecting Acute Myeloid/Lymphoblastic Leukemia. This is the first part of a series of articles that will take you through my experience building a custom classifier with Caffe that should be able to detect Acute Lymphoblastic Leukemia.

This article, Detecting Invasive Ductal Carcinoma with Convolutional Neural Networks, shows how existing deep learning technologies can be utilized to train artificial intelligence (AI) to be able to detect invasive ductal carcinoma (IDC)1 (breast cancer) in unlabeled histology images.

Inception V3 by Google is the 3rd version in a series of Deep Learning Convolutional Architectures. Inception V3 was trained using a dataset of 1000 classes from the original ImageNet dataset which was trained with over 1 million training images. Inception V3 was trained for the ImageNet Large Visual Recognition Challenge where it was a first runner up.

The AML/ALL Classifier Data Augmentation program applies filters to datasets and increases the amount of training / test data available to use. The program is part of the computer vision research and development for the Peter Moss Acute Myeloid/Lymphoblastic (AML/ALL) Leukemia AI Research Project.