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Convolution Properties II01:17

Convolution Properties II

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The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
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Convolution Properties I01:20

Convolution Properties I

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Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
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Protein Networks02:26

Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
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Protein Networks02:26

Protein Networks

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Network Covalent Solids02:18

Network Covalent Solids

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Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
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Adrenergic Receptors: ɑ Subtype01:31

Adrenergic Receptors: ɑ Subtype

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Adrenoceptors are classified into α and ꞵ classes based on their potencies to catecholamine agonists. α-adrenoceptors show the following order of catecholamine potency:
Adrenaline ≥ Noradrenaline >> Isoprenaline
α-adrenoceptors are further divided into α1 and α2-adrenoceptors.
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Related Experiment Video

Updated: Jan 20, 2026

A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes
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Identification of Leukemia Subtypes from Microscopic Images Using Convolutional Neural Network.

Nizar Ahmed1, Altug Yigit1, Zerrin Isik1

  • 1Department of Computer Engineering, Dokuz Eylul University, 35160 Izmir, Turkey.

Diagnostics (Basel, Switzerland)
|August 28, 2019
PubMed
Summary

This study introduces a new convolutional neural network (CNN) approach for diagnosing all leukemia subtypes from blood cell images. Data augmentation significantly improved the CNN model's accuracy in leukemia detection.

Keywords:
convolutional neural networkdata augmentationdeep learningleukemia diagnosismicroscopic blood cells imagesmulti-class classificationrecognizing leukemia subtypes

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Area of Science:

  • Medical Imaging
  • Computational Biology
  • Oncology

Background:

  • Leukemia is a fatal cancer with four main subtypes: acute lymphoid, acute myeloid, chronic lymphoid, and chronic myeloid.
  • Accurate and early diagnosis of leukemia subtypes is crucial for effective treatment and patient outcomes.
  • Microscopic blood cell images offer a potential source for automated leukemia diagnosis.

Purpose of the Study:

  • To develop and evaluate a novel deep learning approach for the automated diagnosis of all four leukemia subtypes using microscopic blood cell images.
  • To investigate the impact of data augmentation techniques on improving the performance of the diagnostic model.
  • To compare the performance of the proposed convolutional neural network (CNN) model against traditional machine learning algorithms.

Main Methods:

  • Utilized two public leukemia datasets (ALL-IDB and ASH Image Bank) comprising microscopic blood cell images.
  • Applied seven distinct image transformation techniques for data augmentation to synthetically increase the training dataset size.
  • Designed and implemented a custom CNN architecture for leukemia subtype classification and compared it with Naive Bayes, SVM, k-NN, and Decision Tree algorithms.
  • Employed 5-fold cross-validation for rigorous model performance evaluation.

Main Results:

  • The CNN model achieved 88.25% accuracy in distinguishing leukemia from healthy samples.
  • The CNN model demonstrated 81.74% accuracy in multiclass classification of all four leukemia subtypes.
  • The proposed CNN model outperformed other traditional machine learning algorithms in leukemia diagnosis tasks.

Conclusions:

  • Convolutional neural networks, enhanced by data augmentation, provide a robust and accurate method for diagnosing all leukemia subtypes from microscopic blood cell images.
  • The developed CNN model shows significant potential for improving automated leukemia diagnostic systems.
  • This approach offers a promising tool for early and precise detection of leukemia, aiding clinical decision-making.