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Computed Tomography01:10

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...

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TTDCapsNet: Tri Texton-Dense Capsule Network for complex and medical image recognition.

Vivian Akoto-Adjepong1, Obed Appiah1, Patrick Kwabena Mensah1

  • 1Department of Computer Science and Informatics, University of Energy and Natural Resources, Sunyani, Ghana.

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Summary
This summary is machine-generated.

A new Tri Texton-Dense CapsNet (TTDCapsNet) model enhances complex and medical image classification. This Capsule Network architecture achieves high accuracy on diverse datasets, outperforming baseline models.

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Artificial Intelligence

Background:

  • Convolutional Neural Networks (CNNs) excel at hierarchical feature learning but require vast datasets.
  • Capsule Networks (CapsNets) perform well with limited data but struggle with complex image recognition.

Purpose of the Study:

  • To introduce a novel Capsule Network architecture, Tri Texton-Dense CapsNet (TTDCapsNet), for improved complex and medical image classification.
  • To address the limitations of existing CNNs and CapsNets in handling complex visual data.

Main Methods:

  • Developed TTDCapsNet, a hierarchical architecture comprising three Texton-Dense CapsNet (TDCapsNet) blocks.
  • Each TDCapsNet integrates a texton detection layer, an eight-layered dense convolution block, and Primary Capsule (PC) and Class Capsule (CC) layers.
  • Employed a routing algorithm to combine feature maps from multiple PCs and CC layers for enhanced classification.

Main Results:

  • Achieved high validation accuracies: 94.90% on fashion-MNIST, 89.09% on CIFAR-10, 95.01% on Breast Cancer, and 97.71% on Brain Tumor datasets.
  • Demonstrated superior performance compared to baseline models and competitive results against state-of-the-art CapsNet models.
  • Confirmed the effectiveness of the routing algorithm and the hierarchical structure in improving classification outcomes.

Conclusions:

  • The proposed TTDCapsNet model is viable for complex real-world image classification tasks.
  • This architecture shows significant potential as an intelligent system for aiding oncologists in disease diagnosis and treatment planning.
  • The study highlights the advancement of Capsule Networks in tackling challenging visual recognition problems.