This blog category focuses on the research were are doing related to detection and early detection of leukemia.

So far our research in this area covers using Convolutional Neural Networks (CNNs) & Generative Adversarial Networks (GANs) for detecting Acute Lymphoblastic Leukemia.

xDNN for SARS-CoV-2 identification in patient CT scans 2021-02-22

xDNN for SARS-CoV-2 identification in patient CT scans

This article is based on the work of Nitin Mane and his GitHub release: HIAS COVID-19 xDNN Classifier.

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Acute Lymphoblastic Leukemia detection with Tensorflow 2020-03-06

Acute Lymphoblastic Leukemia detection with Tensorflow

The final classifier achieves 98 (97.979)% using Tensorflow 2 & Ubuntu/GTX 1050 ti . You can run the classifier independently and classify local images, serve an API endpoint for HTTP requests, or you can use it as part of the VR experience which will be uploaded soon.

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Acute Lymphoblastic Leukemia Papers Evaluation Part 2 Tensorflow 2.0 2020-01-19

Acute Lymphoblastic Leukemia Papers Evaluation Part 2 Tensorflow 2.0

Here we will train the network we created in part 1, using the augmented dataset proposed in the Leukemia Blood Cell Image Classification Using Convolutional Neural Network paper by T. T. P. Thanh, Caleb Vununu, Sukhrob Atoev, Suk-Hwan Lee, and Ki-Ryong Kwon.

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Acute Lymphoblastic Leukemia Papers Evaluation Part 1 2020-01-19

Acute Lymphoblastic Leukemia Papers Evaluation Part 1

Here we will replicate the network architecture and data split proposed in the Acute Leukemia Classification Using Convolution Neural Network In Clinical Decision Support System paper and compare our results.

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Inception V3 Deep Convolutional Architecture For Classifying Acute Myeloid/Lymphoblastic Leukemia 2019-02-17

Inception V3 Deep Convolutional Architecture For Classifying Acute Myeloid/Lymphoblastic Leukemia

Inception V3 by Google is the 3rd version in a series of Deep Learning Convolutional Architectures. Inception V3 was trained using a dataset of 1,000 classes (See the list of classes here) from the original ImageNet dataset which was trained with over 1 million training images, the Tensorflow version has 1,001 classes which is due to an additional "background' class not used in the original ImageNet. Inception V3 was trained for the ImageNet Large Visual Recognition Challenge where it was a first runner up. This article will take you through some information about Inception V3, transfer learning, and how we use these tools in the Acute Myeloid/Lymphoblastic Leukemia AI Research Project.

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Detecting Acute Lymphoblastic Leukemia Using Caffe*, OpenVINO™ and Intel® Neural Compute Stick 2: Part 1 2019-03-09

Detecting Acute Lymphoblastic Leukemia Using Caffe*, OpenVINO™ and Intel® Neural Compute Stick 2: Part 1

As part of my R&D for the Acute Myeloid/Lymphoblastic Leukemia (AML/ALL) AI Research Project, I am reviewing a selection of papers related to using Convolutional Neural Networks (CNN) for detecting AML/ALL. These papers share various ways of creating CNNs, and include useful information about the structure of the layers and the methods used which will help to reproduce the work outlined in the papers. This article will take you through some information about Inception V3, transfer learning, and how we use these tools in the Acute Myeloid/Lymphoblastic Leukemia AI Research Project.

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