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Classification of Leukocytes

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ALNett: A cluster layer deep convolutional neural network for acute lymphoblastic leukemia classification.

Malathy Jawahar¹, Sharen H², Jani Anbarasi L²

¹Leather Process Technology Division, CSIR-Central Leather Research Institute, Chennai, India.

Computers in Biology and Medicine

|August 8, 2022

Summary

This study introduces ALNett, a deep neural network for early Acute Lymphoblastic Leukemia (ALL) detection using microscopic images. ALNett achieved 91.13% accuracy, outperforming other models with lower computational cost.

Keywords:

Computer-aided diagnostic Convolutional neural network Deep learning Leukemia Transfer learning models

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Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning

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Area of Science:

Oncology
Medical Imaging
Artificial Intelligence

Background:

Acute Lymphoblastic Leukemia (ALL) is a significant pediatric cancer, with over 6500 US cases annually.
Early ALL diagnosis is crucial for improving clinical decisions and patient outcomes.
Advancements in AI and big data analytics offer new avenues for rapid and accurate cancer detection.

Purpose of the Study:

To develop and evaluate a deep neural network model, ALNett, for the classification of microscopic white blood cell images for ALL detection.
To leverage depth-wise convolution with varying dilation rates for enhanced feature extraction in ALL diagnosis.
To compare ALNett's performance against established pre-trained models.

Main Methods:

A novel deep neural network, ALNett, was designed using depth-wise convolution and dilation rates.
The model incorporates convolution, max-pooling, and normalization layers for robust feature extraction.
ALNett's performance was benchmarked against VGG16, ResNet-50, GoogleNet, and AlexNet using key metrics.

Main Results:

ALNett achieved the highest classification accuracy at 91.13%.
The model demonstrated a superior F1 score of 0.96.
ALNett exhibited lower computational complexity compared to the pre-trained models evaluated.

Conclusions:

The proposed ALNett model shows significant promise for accurate Acute Lymphoblastic Leukemia categorization.
ALNett outperforms existing pre-trained models in ALL classification from microscopic images.
The model's efficiency and accuracy suggest its potential utility in clinical settings for aiding early ALL diagnosis.