ALLNet: A Hybrid Convolutional Neural Network to Improve Diagnosis of
Acute Lymphocytic Leukemia (ALL) in White Blood Cells
- URL: http://arxiv.org/abs/2108.08195v1
- Date: Wed, 18 Aug 2021 15:24:53 GMT
- Title: ALLNet: A Hybrid Convolutional Neural Network to Improve Diagnosis of
Acute Lymphocytic Leukemia (ALL) in White Blood Cells
- Authors: Sai Mattapalli, Rishi Athavale
- Abstract summary: The ALL Challenge dataset contains 10,691 images of white blood cells which were used to train and test the models.
AllNet, the proposed hybrid convolutional neural network architecture, consists of a combination of the VGG, ResNet, and Inception models.
In the test set, ALLNet achieved an accuracy of 92.6567%, a sensitivity of 95.5304%, a specificity of 85.9155%, an AUC score of 0.966347, and an F1 score of 0.94803.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Due to morphological similarity at the microscopic level, making an accurate
and time-sensitive distinction between blood cells affected by Acute
Lymphocytic Leukemia (ALL) and their healthy counterparts calls for the usage
of machine learning architectures. However, three of the most common models,
VGG, ResNet, and Inception, each come with their own set of flaws with room for
improvement which demands the need for a superior model. ALLNet, the proposed
hybrid convolutional neural network architecture, consists of a combination of
the VGG, ResNet, and Inception models. The ALL Challenge dataset of ISBI 2019
(available here) contains 10,691 images of white blood cells which were used to
train and test the models. 7,272 of the images in the dataset are of cells with
ALL and 3,419 of them are of healthy cells. Of the images, 60% were used to
train the model, 20% were used for the cross-validation set, and 20% were used
for the test set. ALLNet outperformed the VGG, ResNet, and the Inception models
across the board, achieving an accuracy of 92.6567%, a sensitivity of 95.5304%,
a specificity of 85.9155%, an AUC score of 0.966347, and an F1 score of 0.94803
in the cross-validation set. In the test set, ALLNet achieved an accuracy of
92.0991%, a sensitivity of 96.5446%, a specificity of 82.8035%, an AUC score of
0.959972, and an F1 score of 0.942963. The utilization of ALLNet in the
clinical workspace can better treat the thousands of people suffering from ALL
across the world, many of whom are children.
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